Zettabytes is developing a service native observability platform for micro-services/containers, providing near real-time health status of all services: Cloud, On-Prem, et al

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This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Hatem A. Alharbi, CSchool of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom;
(2) Taisir E.H. Elgorashi, School of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom;
(3) Jaafar M.H. Elmirghani, School of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom. Table of Links Abstract & Introduction Related Works Repealing Net Neutrality Profit-Driven Model Results Conclusions & References Biographies Abstract Network neutrality (net neutrality) is the principle of treating equally all Internet traffic regardless of its source, destination, content, application or other related distinguishing metrics. Under net neutrality, ISPs are compelled to charge all content providers (CPs) the same per Gbps rate despite the growing profit achieved by CPs. In this paper, we study the impact of the repeal of net neutrality on communication networks by developing a techno-economic Mixed Integer Linear Programming (MILP) model to maximize the potential profit ISPs can achieve by offering their services to CPs. We focus on video delivery as video traffic accounts for 78% of the cloud traffic. We consider an ISP that offers CPs different classes of service representing typical video content qualities including standard definition (SD), high definition (HD) and ultra-high definition (UHD) video. The MILP model maximizes the ISP profit by optimizing the prices of the different classes according to the users demand sensitivity to the change in price, referred to as Price Elasticity of Demand (PED). We analyze how PED impacts the profit in different CP delivery scenarios in cloud-fog architectures. The results show that the repeal of net neutrality can potentially increase ISPs profit by a factor of 8 with a pricing scheme that discriminates against data intensive content. Also, the repeal of net neutrality positively impacts the network energy efficiency by reducing the core network power consumption by 55% as a result of suppressing data intensive content compared to the net neutrality scenario. Index Terms— Net neutrality, AT&T, IP over WDM networks, profit, power consumption. I. INTRODUCTION Network (net) neutrality regulations prohibit ISPs from applying different treatment to IP packets based on their content e.g. prioritizing, blocking or throttling certain Internet content or allowing quality differentiation. Net neutrality, which was scrapped by the US Federal Communications Commission (FCC) in December 2017, has been the subject of remarkable debate in recent years between ISPs and CPs with each side trying to exploit their assets and expand their profit and influence. The debate is fueled by the rapidly escalating demand for CPs services as a result of the interconnection between Internet and broadcasting markets. Cisco forecasts [1] that by 2021, annual global Internet traffic will hit 2.2 Zettabytes per month and CPs datacenters will be the source of 71% of this traffic. Online video services are the primary cause of this accelerated growth in Internet traffic. Video streaming is poised to consume 78% of the total CPs bandwidth with 75% of Internet video traffic originating from higher video services quality (HD and UHD). Proponents of preferential treatment of Internet traffic complain that the increasing demand for data-intensive content creates a significant burden on the communication network. They argue that removing net neutrality will give ISPs further control of their infrastructure, which is crucial in order to improve QoS and reduce security threats. Another argument is that a significant fraction of the profit of this tremendously growing market is seized by CPs whereas ISPs act as a transit or transport medium into CPs customers. In the US, the quarterly profit margin of AT&T (an ISP) has been almost stable over the last six years whereas Netflix (a CP) profit margin has risen up in rapid pace from 0.7% to 9.8% within the same period [2], [3]. In contrast, advocates warn that removing net neutrality will slow down the innovation in the Internet and its content and will limit the content competition by disadvantaging small businesses, and subsequently, diminish online services. Deploying traffic discrimination in video delivery services has many challenges, e.g. detecting video packets and enforcing a policy on a certain video quality. Traffic discrimination in IP communication networks has been surveyed intensively in the literature. Several traffic management practices have been surveyed in [4]. The authors highlighted that traffic discrimination taxonomy has four features: (i) characteristics or condition of the traffic (e.g. based on content, protocol or source/destination); (ii) traffic classification (e.g. based on flow rate, header information or routing); (iii) mechanism of discrimination (e.g. modify, delay, drop or block); and (iv) perceived discrimination by end-users. Video traffic can be analyzed using two mechanisms; deep packet inspection (DPI) [5], [6] or traffic profiling [7], [8]. DPI examines the data packets that are sent over the network and traffic profiling detects abnormal network traffic by comparing new traffic against previous traffic profile. For example, an alarm can be triggered if the data rate transmitted over the network (measured in bps) spikes above the desired data rate, which could indicate an increase in data rate. QoS for video services delivery can be applied either by reserving network bandwidth for video packets (e.g. using IntServ) or labelling video content as high priority e.g. by applying Differentiated Services (e.g. using DiffServ) [9]. The Internet ecosystem is complex with many stakeholders. As illustrated in Fig. 1, the main stakeholders in the Internet ecosystem are; ISPs, CPs, content delivery networks (CDNs) and end-users. Users pay ISPs a subscription fee to get Internet access and subscribe to CPs (if required) to access their content. CPs subscribe to a CDN to access storage and processing capacity and to deliver their content to customers. CDNs are responsible for sending CPs content at large scale over ISPs network infrastructure, e.g. the CP Netflix collaborates with the CDN Amazon Web Services (AWS) to reach their customers [10]. ISPs play as the key intermediary in the delivery process as they provide the required connectivity between users and content. Most ISPs such as AT&T [11] and Comcast [12] are now providing CDN services in additional to networking services. To simplify our analysis, we consider a direct relationship between ISP and CPs. Due to net neutrality regulations, current pricing policy of ISP networking services applies a fixed charge which is not linked with bitrate usage. For example, in the US, AT&T uses a fixed pricing model by charging CPs $3,282 per 10 Gbps per month [13] regardless of the content type transferred to users (either UHD video content or a simple text message). The rest of this paper is organized as follows: Section II briefly summarizes related work. We describe the pricing scheme we used in this paper and the profit-driven model we adopted in Section III. Our results are presented in Section IV. In Section V, we provide concluding remarks. This paper is available on arxiv under CC 4.0 license. Authors: (1) Hatem A. Alharbi, CSchool of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom; (2) Taisir E.H. Elgorashi, School of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom; (3) Jaafar M.H. Elmirghani, School of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom. This paper is available on arxiv under CC 4.0 license. available on arxiv Authors: Authors: (1) Hatem A. Alharbi, CSchool of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom; (2) Taisir E.H. Elgorashi, School of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom; (3) Jaafar M.H. Elmirghani, School of Electronic and Electrical Engineering, University of Leeds, LS2 9JT, United Kingdom. Table of Links Abstract & Introduction Abstract & Introduction Related Works Related Works Repealing Net Neutrality Repealing Net Neutrality Profit-Driven Model Results Profit-Driven Model Results Conclusions & References Conclusions & References Biographies Biographies Abstract Network neutrality (net neutrality) is the principle of treating equally all Internet traffic regardless of its source, destination, content, application or other related distinguishing metrics. Under net neutrality, ISPs are compelled to charge all content providers (CPs) the same per Gbps rate despite the growing profit achieved by CPs. In this paper, we study the impact of the repeal of net neutrality on communication networks by developing a techno-economic Mixed Integer Linear Programming (MILP) model to maximize the potential profit ISPs can achieve by offering their services to CPs. We focus on video delivery as video traffic accounts for 78% of the cloud traffic. We consider an ISP that offers CPs different classes of service representing typical video content qualities including standard definition (SD), high definition (HD) and ultra-high definition (UHD) video. The MILP model maximizes the ISP profit by optimizing the prices of the different classes according to the users demand sensitivity to the change in price, referred to as Price Elasticity of Demand (PED). We analyze how PED impacts the profit in different CP delivery scenarios in cloud-fog architectures. The results show that the repeal of net neutrality can potentially increase ISPs profit by a factor of 8 with a pricing scheme that discriminates against data intensive content. Also, the repeal of net neutrality positively impacts the network energy efficiency by reducing the core network power consumption by 55% as a result of suppressing data intensive content compared to the net neutrality scenario. Index Terms — Net neutrality, AT&T, IP over WDM networks, profit, power consumption. Index Terms I. INTRODUCTION Network (net) neutrality regulations prohibit ISPs from applying different treatment to IP packets based on their content e.g. prioritizing, blocking or throttling certain Internet content or allowing quality differentiation. Net neutrality, which was scrapped by the US Federal Communications Commission (FCC) in December 2017, has been the subject of remarkable debate in recent years between ISPs and CPs with each side trying to exploit their assets and expand their profit and influence. The debate is fueled by the rapidly escalating demand for CPs services as a result of the interconnection between Internet and broadcasting markets. Cisco forecasts [1] that by 2021, annual global Internet traffic will hit 2.2 Zettabytes per month and CPs datacenters will be the source of 71% of this traffic. Online video services are the primary cause of this accelerated growth in Internet traffic. Video streaming is poised to consume 78% of the total CPs bandwidth with 75% of Internet video traffic originating from higher video services quality (HD and UHD). Proponents of preferential treatment of Internet traffic complain that the increasing demand for data-intensive content creates a significant burden on the communication network. They argue that removing net neutrality will give ISPs further control of their infrastructure, which is crucial in order to improve QoS and reduce security threats. Another argument is that a significant fraction of the profit of this tremendously growing market is seized by CPs whereas ISPs act as a transit or transport medium into CPs customers. In the US, the quarterly profit margin of AT&T (an ISP) has been almost stable over the last six years whereas Netflix (a CP) profit margin has risen up in rapid pace from 0.7% to 9.8% within the same period [2], [3]. In contrast, advocates warn that removing net neutrality will slow down the innovation in the Internet and its content and will limit the content competition by disadvantaging small businesses, and subsequently, diminish online services. Deploying traffic discrimination in video delivery services has many challenges, e.g. detecting video packets and enforcing a policy on a certain video quality. Traffic discrimination in IP communication networks has been surveyed intensively in the literature. Several traffic management practices have been surveyed in [4]. The authors highlighted that traffic discrimination taxonomy has four features: (i) characteristics or condition of the traffic (e.g. based on content, protocol or source/destination); (ii) traffic classification (e.g. based on flow rate, header information or routing); (iii) mechanism of discrimination (e.g. modify, delay, drop or block); and (iv) perceived discrimination by end-users. Video traffic can be analyzed using two mechanisms; deep packet inspection (DPI) [5], [6] or traffic profiling [7], [8]. DPI examines the data packets that are sent over the network and traffic profiling detects abnormal network traffic by comparing new traffic against previous traffic profile. For example, an alarm can be triggered if the data rate transmitted over the network (measured in bps) spikes above the desired data rate, which could indicate an increase in data rate. QoS for video services delivery can be applied either by reserving network bandwidth for video packets (e.g. using IntServ) or labelling video content as high priority e.g. by applying Differentiated Services (e.g. using DiffServ) [9]. The Internet ecosystem is complex with many stakeholders. As illustrated in Fig. 1, the main stakeholders in the Internet ecosystem are; ISPs, CPs, content delivery networks (CDNs) and end-users. Users pay ISPs a subscription fee to get Internet access and subscribe to CPs (if required) to access their content. CPs subscribe to a CDN to access storage and processing capacity and to deliver their content to customers. CDNs are responsible for sending CPs content at large scale over ISPs network infrastructure, e.g. the CP Netflix collaborates with the CDN Amazon Web Services (AWS) to reach their customers [10]. ISPs play as the key intermediary in the delivery process as they provide the required connectivity between users and content. Most ISPs such as AT&T [11] and Comcast [12] are now providing CDN services in additional to networking services. To simplify our analysis, we consider a direct relationship between ISP and CPs. Due to net neutrality regulations, current pricing policy of ISP networking services applies a fixed charge which is not linked with bitrate usage. For example, in the US, AT&T uses a fixed pricing model by charging CPs $3,282 per 10 Gbps per month [13] regardless of the content type transferred to users (either UHD video content or a simple text message). The rest of this paper is organized as follows: Section II briefly summarizes related work. We describe the pricing scheme we used in this paper and the profit-driven model we adopted in Section III. Our results are presented in Section IV. In Section V, we provide concluding remarks.

Impact of the Net Neutrality Repeal on Communication Networks: Abstract & Introduction

This paper is available on arxiv under CC 4.0 license. Authors: (1) Vogt, Lars, TIB Leibniz Information Centre for Science and Technology; (2) Konrad, Marcel, TIB Leibniz Information Centre for Science and Technology; (3) Prinz, Manuel, TIB Leibniz Information Centre for Science and Technology. Table of Links Abstract & Introduction Interoperability Semantic interoperability and what natural languages like English can teach us Requirements for successfully communicating terms and statements Parallels between the structure of natural language statements and data schemata with implications for semantic interoperability What makes a term a good term and a schema a good schema? The need for a machine-actionable Rosetta Stone for (meta)data that acts as an interlingua for specifying reference terms and reference schemata to support cognitive and semantic interoperability Rosetta Stone and machine-readability: UPRIs, XML Schema datatypes, and RDF for communicating terms, datatypes, and statements Rosetta Stone and machine-interpretability: Wikidata and a modeling paradigm for (meta)data statements based on English Rosetta Stone and semantic interoperability: Specifying term mappings and schema crosswalks Rosetta Stone and cognitive interoperability: Specifying display templates and using a query builder Discussion Related work Conclusion, Acknowledgements, & References Abstract In order to effectively manage the overwhelming influx of data, it is crucial to ensure that data is findable, accessible, interoperable, and reusable (FAIR). While ontologies and knowledge graphs have been employed to enhance FAIRness, challenges remain regarding semantic and cognitive interoperability. We explore how English facilitates reliable communication of terms and statements, and transfer our findings to a framework of ontologies and knowledge graphs, while treating terms and statements as minimal information units. We categorize statement types based on their predicates, recognizing the limitations of modeling non-binary predicates with multiple triples, which negatively impacts interoperability. Terms are associated with different frames of reference, and different operations require different schemata. Term mappings and schema crosswalks are therefore vital for semantic interoperability. We propose a machine-actionable Rosetta Stone Framework for (meta)data, which uses reference terms and schemata as an interlingua to minimize mappings and crosswalks. Modeling statements rather than a human-independent reality ensures cognitive familiarity and thus better interoperability of data structures. We extend this Rosetta modeling paradigm to reference schemata, resulting in simple schemata with a consistent structure across statement types, empowering domain experts to create their own schemata using the Rosetta Editor, without requiring knowledge of semantics. The Editor also allows specifying textual and graphical display templates for each schema, delivering human-readable data representations alongside machine-actionable data structures. The Rosetta Query Builder derives queries based on completed input forms and the information from corresponding reference schemata. This work sets the conceptual ground for the Rosetta Stone Framework that we plan to develop in the future. Introduction In the current era, we are witnessing an exponential growth in the generation and consumption of data. Statista’s projections from 2020 indicate that the global data creation, capture, copying, and consumption will reach an estimated 97 zettabytes in 2022, with a staggering daily volume of approximately 328.77 billion terabytes by 2023, reflecting a notable 23.71% increase over the preceding year, with videos accounting for over 50% of the global traffic (1,2). Additionally, data production in the last two years accounted for a striking 90% of the entire data generated worldwide in 2016 (3), illustrating the rapid pace of data expansion. The doubling of the overall data volume every three years (4) further accentuates the magnitude of this exponential growth. Simultaneously, the scholarly domain is witnessing a surge in publications, with an annual output of over 7 million academic papers (5). These figures emphasize the urgency of harnessing machine support, as without it, the sheer volume of (meta)data threatens to overwhelm and impede meaningful insights. With this in mind, it is essential to facilitate machine-actionable (meta)data in scientific research, so that machines assist researchers in identifying relevant (meta)data pertaining to a specific research question. Moreover, enhancing the machine-actionability of (meta)data offers a potential solution to the reproducibility crisis in science (6), enabling the availability, findability, and usability of raw (meta)data (7). Considering the substantial global investment in research and development, which reached a staggering sum of $1,609,214 billion in 2021 (8), failure to render the corresponding output of (meta)data machine-actionable could potentially result in redundant research efforts. By enabling machine-actionable (meta)data, substantial savings can be achieved, redirecting resources towards novel and non-repetitive research endeavors. However, the concept of machine-actionable (meta)data requires clarification. Looking at the definition of machine-actionable (see Box 1), it is evident that this attribute cannot be simplified as a mere Boolean property. Instead, it exists on a spectrum, allowing for degrees of machine-actionability. Numerous operations can potentially be applied to a given set of (meta)data, and the ability to apply even a single operation would suffice to classify the (meta)data as machine-actionable. Consequently, specifying the set of operations that can be applied to the (meta)data, along with the corresponding tools or code, is more meaningful than labeling the entire set as machine-actionable. For instance, one could state that “Dataset A is machine-actionable with respect to operation X using tool Y”. It is worth noting that reading a dataset could be considered an operation itself. Therefore, datasets documented in formats as PDF, XML, or even ASCII files can be considered machine-readable and, to some extent, already machine-actionable. Moreover, if a dataset is machine-readable, search operations can be performed on it, enabling the identification of specific elements through label matching, for example. The success of search operations serves as a measure of the findability of (meta)data. Similarly to machine-actionability, findability cannot be characterized as a Boolean property. Machine-readable (meta)data can be found through label matching, while interpretable (meta)data can be found through their meaning, referent, or contextual information. Thus, (meta)data that are readable but not interpretable possess limited findability. It is important to emphasize that our definition of machine-actionability, as outlined in Box 1, strictly depends on machine-interoperability. Consequently, machine-reading of a dataset and machine-searching based on label matching are not considered proper examples for operations that fulfill the requirements of machine-actionability. In 2016, the were introduced, providing a framework to assess the extent to which (meta)data are Findable, Accessible, Interoperable, and Reusable for both machines and humans alike (11). These principles have gained increasing attention from the research, industry, and knowledge management tool development communities in recent years (11–16). Furthermore, stakeholders in science and research policy have recognized the significance of the FAIR Principles. The economic impact of FAIR research (meta)data was estimated by the European Union (EU) in 2018, revealing that the lack of FAIR (meta)data costs the EU economy at least 10.2 billion Euros annually. Taking into account the positive effects of FAIR (meta)data on data quality and machine-readability, an additional 16 billion Euros were estimated (17). Consequently, the High Level Expert Group of the European Open Science Cloud (EOSC) recommended the establishment of an Internet of FAIR Data and Services (IFDS) (18). The IFDS aims to enable data-rich institutions, research projects, and citizen-science initiatives to make their (meta)data accessible in accordance with the FAIR Guiding Principles, while retaining control over ethical, privacy, and legal aspects of their (meta)data (following Barend Mons’ data visiting as opposed to data sharing (19)). Achieving this goal requires the provision of rich machine-actionable (meta)data, their organization into FAIR Digital Objects (20,21), each identifiable by a Unique Persistent and Resolvable Identifier (UPRI), and the development of suitable concepts and tools for human-readable interface outputs and search capabilities. Although progress has been made toward building the IFDS (see the GO FAIR Initiative and EOSC), the current state of the FAIRness of (meta)data in many data-rich institutions and companies is still far from ideal. FAIR Guiding Principles for (meta)data The increasing volume, velocity, variety, and complexity of data present significant challenges that traditional methods and techniques for handling, processing, analyzing, managing, storing, and retrieving (meta)data struggle to address effectively within a reasonable timeframe (22). However, knowledge graphs in conjunction with ontologies, offer a promising technical solution for implementing the FAIR Guiding Principles, thanks to their transparent semantics, highly structured syntax, and standardized formats (23,24). Knowledge graphs represent instances, classes, and their relationships as resources with their own UPRIs. These UPRIs are employed to denote relationships between entities using the triple syntax of Subject-Predicate-Object. Each particular relationship is thus modeled as a structured set of three distinct data points. In contrast, relational databases model entity relationships between data table columns and not between individual data points. Consequently, knowledge graphs outperform relational databases in handling complex queries on densely connected data, which is often the case with research (meta)data. Therefore, knowledge graphs are particularly well suited for FAIR research and development, as well as any task requiring efficient and detailed retrieval of (meta)data. Nonetheless, employing a knowledge graph to document (meta)data does not guarantee adherence to FAIR Principles. Achieving FAIRness necessitates additional guidelines, such as consistent usage of the same semantic data model for identical types of (meta)data statements to ensure schematic interoperability (10), as well as organizing (meta)data into FAIR Digital Objects (20,21). Knowledge graphs, being a relatively new concept and technology, introduce their own specific technical, conceptual, and societal challenges. This is evident in the somewhat ambiguous nature of the knowledge graph concept (23) and the absence of commonly accepted standards, given the diverse technical and conceptual incarnations ranging from labeled property graphs like Neo4J to approaches based on the Resource Description Framework (RDF), employing RDF stores and applications of description logic using the Web Ontology Language (OWL). Regardless of these considerations, it can be argued that the demand for FAIR and machine-actionable (meta)data presupposes their During such communication processes, preserving the meaning and reference of the message between sender and receiver is crucial, requiring both parties to share the same background knowledge, encompassing lexical competences, syntax and grammar rules, and relevant contextual knowledge. successful communication between machines and between humans and machines. In this paper, we lay the conceptual groundwork for a future machine-actionable Rosetta Stone Framework designed to achieve cognitive and semantic interoperability of (meta)data. We begin by examining the notion of interoperability and emphasizing the necessity of cognitive interoperability. Drawing inspiration from natural languages like English, we explore how semantic interoperability can be understood by analyzing the way terms and statements convey meaning and information. Recognizing terms and statements as basic units of information, we investigate the linguistic structures that ensure reliable information communication and draw parallels to the structures found in (meta)data schemata. This analysis yields insights into achieving semantic interoperability of terms and statements. Our main argument centers around the requirement for a machine-actionable Rosetta Stone Framework for (meta)data, which addresses terminological and propositional interoperability. This framework leverages reference terms and reference schemata as an interlingua to establish term mappings and schema crosswalks, facilitating cognitive and semantic interoperability. The emphasis lies on a modeling paradigm that enables machine-interpretability of (meta)data, prioritizing their findability and interoperability over reasoning capabilities. This prioritization opens up new avenues for modeling by shifting away from the paradigm frequently applied in science that focuses on modeling a human-independent reality. Instead, the Rosetta Modeling Paradigm models the structure of statements to enhance efficient and reliable information communication. We propose two alternative versions of a Rosetta Modeling Approach: a minimal light version and a full version that supports versioning of statements and tracks the editing history of each statement within a knowledge graph. Furthermore, we discuss the specifications for a Rosetta Editor, a tool that will allow domain experts to create new reference schemata without requiring expertise in semantics or computer science. The Rosetta Editor will also enable the specification of display templates for human-readable text-based and graph-based representation of (meta)data. Additionally, we propose a Rosetta Query Builder that allows users to write queries without the need for a graph query language. Finally, we provide an overview of the Rosetta Framework and explore related work in the field before presenting our concluding remarks. It is important to note that this paper represents conceptual work and serves as a foundation for future development, testing, and implementation of the envisioned Rosetta Stone Framework.

Towards a Rosetta Stone for (meta)data: Abstract & Introduction

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Getting Started with the Weaviate Vector Search Engine Everybody who works with data in any way shape or form knows that one of the most important challenges is searching for the correct answers to your questions. There is a whole set of excellent (open source) search engines available but there is one thing that they can’t do, search and related data based on context. 33. 8 Ways to Improve the CLS Score in Google’s Core Web Vitals The Cumulative Layout Shift (CLS) of a website is important to user experience, which is why it’s one of three metrics included in Google’s new Core Web Vitals. 34. Build A Web Crawler with Search bar Using Wget and Manticore [A Step By Step Guide] Hi everyone. In this article we are going to talk about how can you write a simple web scraper and a little search application using well known existing technologies which you perhaps didn’t know they can do that. 35. Jina, a Deep Learning-Powered Search Framework, Can Help You Build Your Neural Search Use Jina to search text or images with the power of deep learning. 36. SEO, Data Science & Correlative Analysis For Google Organic Traffic This article will touch on how data science can be used in SEO and look at how correlative analysis should be used during the content creation process. For those not familiar with these topics, there will be examples and pictures but as should be expected when covering any complicated topic, the scope of the article will be limited to the main purpose. 37. To be Relevant or not to be: a Search Story about Precision and Recall With the amount of data created growing exponentially each year and forecasted to reach 59 zettabytes in 2020 and more than 175 zettabytes by 2025, the importance of discovering and understanding this data will continue to be, even more than before, a decisive and competitive differentiator for many companies. 38. How to Use Fuzzy Query Matches in Elasticsearch Typo is something that often happens and can reduce user’s experience, fortunately, Elasticsearch can handle it easily with Fuzzy Query. 39. 3 Underused Structured Data Schema Markups Schema markup using structured data is a wonderful thing. Not only does it help Google Search better understand the content of a page, it can also display relevant snippets of information in search results. 40. Highlighting in Search Results In this tutorial you will learn how to highlight search results in Manticore Search. You can benefit from search results highlighting if you want to improve readability of search results in your application or a web site. 41. To be Relevant or not to be: a Search Story about Precision and Recall With the amount of data created growing exponentially each year and forecasted to reach 59 zettabytes in 2020 and more than 175 zettabytes by 2025, the importance of discovering and understanding this data will continue to be, even more than before, a decisive and competitive differentiator for many companies. 42. Fundamentals of Full-Text Operators and Basic Search In this tutorial, we will explore full-text search operators available in Manticore Search. 43. Dead Letter Queue no Elastic Stack Melhore a obsevabilidade da sua Stack Elastic, baseado em fatos reais Thank you for checking out the 43 most read stories about Search on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

43 Stories To Learn About Search

107 Stories To Learn About Search Engine

164 Stories To Learn About Nlp

Consumers today can interact with businesses in more ways than ever before. That gives companies many options when reaching out to new and returning customers, but the savviest marketers don’t just choose one or two options. They capitalize on all of them through omnichannel customer experiences. The rise of e-commerce has also made it easier to sell to a broader market. As a result, competition has grown fierce in many markets, so businesses have to do all they can to stand out. An effective omnichannel strategy is an excellent way to do just that. What Is an Omnichannel Customer Experience? An omnichannel marketing approach creates a consistent experience across all available channels. Whether people interact with a company on their phones, computers, TVs, or brick-and-mortar stores, they’ll get the same experience. Some channels may be more effective than others — email marketing is still the — but not every user uses every channel. Some people may exclusively see a brand on social media, others will only interact through the official website and more may use a mix of everything. Making the experience the same across all platforms ensures everyone gets the same message and journey regardless of these differences. best method for making sales Omnichannel provides more data to understand the market and helps businesses reach more customers, create , and boost customer loyalty. Companies today can’t afford to overlook those benefits. a familiar and clear brand image How to Optimize Omnichannel Of course, omnichannel marketing’s effectiveness depends on how well businesses can use it. Making a great omnichannel experience may be a little more complicated than other methods, but here’s how marketers can optimize theirs. 1. Know Your Customers Knowing the target market is the first step in creating an omnichannel strategy. The whole point of omnichannel is to create a consistent brand image and experience, so if this image doesn’t gel with customers, the plan is doomed from the start. If a business already has a customer base, it should poll them. Ask what they like about the brand, their experience, and where they think it could improve. Brands underestimate how often customers have bad experiences by , so this first-hand feedback is invaluable. an average of 38% Looking at data outside of customer surveys is important, too. Companies should examine how much time their customers spend on different channels to learn which areas may yield the best results. Similarly, look at how users interact with various content types across channels. 2. Map Out the Customer Journey As businesses look at their customers and how they act across different channels, they should outline the customer journey. Pay attention to where users are and what they do at key sales funnel stages. Knowing this will inform more effective content at each phase. The journey may look different from user to user, but some overall trends should emerge. Brands can then adapt their marketing materials on each channel to fit the stage of the funnel customers typically are in when engaging with content on that channel. Every stage of the customer journey needs attention, but experts agree in marketing. Mapping these journeys will reveal what channels and behaviors on those platforms typically coincide with these first impressions. Companies can then focus their efforts here to ensure success from the beginning. first impressions matter the most 3. Keep Content Consistent But Not Identical Omnichannel experiences should be seamless across every step of the customer journey on any channel. Someone should be able to interact with the brand on social media on their phone, then pick up right where they left off in a physical store. However, there is such a thing as too much consistency. Each channel has unique strengths and weaknesses, so marketing materials should lean into these. That means content can’t be identical across every channel in every instance. It should provide a consistent image and experience but vary slightly depending on what the specific platform enables. Taking the brand’s TV ad and posting it on social media provides a consistent but unoptimized experience. A better strategy would take advantage of social media’s interactive nature. Instead, a company could use the same graphics and themes from the TV ad but try a live-streamed version or poll the audience to give them control over future content with the same characters. 4. Capitalize on Technology Managing content across multiple channels is complicated — especially when adapting it to each channel’s unique features. However, businesses can work around that complication by embracing newer technologies. Automation and AI are some of the most helpful, as they save time in many areas. Generative AI tools like ChatGPT or draft social media posts to let companies push this communication out faster. Email marketing tools can automatically reply to different interactions to keep potential customers engaged. can create personalized customer interactions Chatbots are another helpful tool — especially chatbot platforms that can pull data from multiple channels. Feeding omnichannel data into chatbots will let them provide the same help to customers even when they talk to them on different platforms, boosting the omnichannel experience. 5. Keep Up With Changing Trends It’s also crucial to remember an omnichannel experience should be dynamic. Businesses can’t set a strategy once and leave it alone. An effective omnichannel experience changes with its customers, adapting to new trends and old shortcomings. The secret to doing this effectively lies within the omnichannel strategy itself. When brands interact with customers across multiple channels, they’ll have a much wider range of user data to pull from. The world generates more than a year, offering plenty of information to show what users do and prefer. 64 zettabytes of data Businesses should routinely gather data from all channels and use this information to review their omnichannel strategy’s efficacy. Remember that what works well today may not work tomorrow. If a once-effective method starts declining, it’s okay to change it — evolving with the times will ensure a better customer experience. Just be sure to change consistently across all channels. Businesses Today Need Omnichannel Experiences People today rarely see and interact with companies on just one platform. Even if they did, everyone has unique habits, so only focusing on one channel means missing out on huge market segments elsewhere. In today’s hyper-connected world, marketing strategies need to be similarly connected. Following these steps to create an effective omnichannel customer experience is crucial to staying competitive in modern markets.

How to Improve Your Omnichannel Customer Experience

Let's learn about via these 40 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Elasticsearch /Learn Repo 1. A Look at Using Elixir Streams, Elasticsearch & AWS S3 Utilizing Elixir Streams, Elasticsearch, and AWS S3 2. AWS ECS vs AWS Lambda Compared Comparing cloud services? Read our Lambda vs ECS guide. Consider programming language, pricing, and the benefits. 3. Solve Several Problems At The Same Time With Kibana Tool Are you a software tester? This is where you find out more about using different reading logs in Kibana in order to better track and understand the errors. 4. Clickhouse vs Elasticsearch vs Manticore Search Query Times With a 1.7B NYC Taxi Rides Benchmark New York City (NYC) taxi rides are probably the most commonly used benchmark in the area of data analytics. 5. Logging Everything in JSON Format Logging and monitoring are like Tony Stark and his Iron Man suit, the two will go together. Similarly, logging and monitoring work best together because they complement each other well. 6. Dead Letter Queue no Elastic Stack Melhore a obsevabilidade da sua Stack Elastic, baseado em fatos reais 7. Analyzing 110 Million Comments from Hacker News In this article, we’ll observe another test with1.1M Hacker News curated comments with numeric fields 8. Building a k-NN Similarity Search Engine using Amazon Elasticsearch and SageMaker Amazon Elasticsearch Service recently added support for k-nearest neighbor search. It enables you to run high scale and low latency k-NN search across thousands of dimensions with the same ease as running any regular Elasticsearch query. 9. Jaeger Persistent Storage with Elasticsearch, Cassandra and Kafka Running systems in production involve requirements for high availability, resilience and recovery from failure. When running cloud-native applications this becomes even more critical, as the base assumption in such environments is that compute nodes will suffer outages, Kubernetes nodes will go down and microservices instances are likely to fail, yet the service is expected to remain up and running. 10. How to Deploy & Monitor Honeypots on GCP with Kibana [Tutorial] One of my favourite areas of cybersecurity is SIEM (Security Incident Event Management). In 2017 I wrote a post on how I got a role in cyber security, one of my recommendations was using the Elastic Stack as a SIEM as a start-off point for those looking to understand log analysis and how to investigate incidents. But one of the main gripes people had was, where can they get data to work on in their home environments. This post will focus on setting up a honeypot that already utilises the ELK Stack… 11. Using Jest to Mock Elasticsearch Test your elastic queries like a pro using jest! 12. Custom TraceID in Elastic APM Elastic APM is extensively useful in monitoring the lifecycle of a HTTP request in a system especially in µservices architecture. Wide variety of web frameworks and databases are supported which is useful in tracking the request up to DB calls. The documentation is simple and concise which makes it easy to instrument the application. This article aims to help or at least make it easy to trace the HTTP request lifecycle after instrumentation. Golang is used in this article for code snippets but the concept can be extended to other languages as well. 13. A 101 on ElastAlert &  How To Set It Up Simple Framework for Alerting anomalies, spikes and other patterns from data in elasticsearch. 14. PGSync Introduction: Real-time Integration Tool For PostgreSQL And Elasticsearch PGSync is a change data capture tool for moving data from Postgres to Elasticsearch. It allows you to keep Postgres as your source-of-truth and expose structured denormalized documents in Elasticsearch. 15. Manticore is a Faster Alternative to Elasticsearch in C++ Manticore Search is a faster alternative to Elasticsearch written in C++ with a 21-year history 16. Grafana Vs. Kibana Vs. Knowi: Battle Royale 2020 Intro: Grafana vs Kibana vs Knowi 17. How to Use Fuzzy Query Matches in Elasticsearch Typo is something that often happens and can reduce user’s experience, fortunately, Elasticsearch can handle it easily with Fuzzy Query. 18. GitHub Actions CI config for MySQL, Redis, Elasticsearch in Ruby on Rails project with RSpec tests How to run parallel tests with Github Actions jobs for Rails project with MySQL, Redis, Elasticsearch. 19. How To Create a Simple Autocomplete Field And Connect it With Elasticsearch Autocomplete is a feature to predict the rest of a word a user is typing. It is an important feature to implement that can improve the user’s experience of your product. 20. How Percolate Queries in Elasticsearch Make Alerting a Breeze Once upon a time, a company I worked for had a problem: We had thousands of messages flowing through our data pipeline each second, and we want to be able to send email and SMS alerts to ours users when messages matching specific criteria were seen. 21. Analyzer in Elasticsearch: An Introduction If we want to create a good search engine with Elasticsearch, knowing how Analyzer works is a must. A good search engine is a search engine that returns relevant results. When the user queried something in our Search Engine, we need to return the documents relevant to the user query. 22. Digging into Postgres's Lesser Known Features Postgres Handles More than You Think 23. Create a Full Autocomplete Search Application with Elasticsearch, Kibana, NestJS and React In this article, I will be walking you through how to set up elasticsearch on your PC. 24. Utilizing the Elasticsearch Snapshot Module for Databackups on Azure blob Storage While running a self managed elasticsearch cluster like any other database, it's important to make provisions for data backups. Data backups on Elasticsearch can't be done by simply copying elasticsearch data files from one disk to another, this tutorial guides you through making the best use of the Elasticsearch snapshot module for creating cluster snapshots and leverages the Azure blob storage for securely storing your backed up data. Also besides backing up data, the snapshot api also comes in handy for migrating data from one cluster to another. 25. Using KSQL Stream Processing & Real-Time Databases to Analyze Kafka Streaming Data [A How-To Guide] Intro 26. How To Master Elasticsearch Query DSL Photo by Evgeni Tcherkasski on Unsplash 27. How To Develop Your Custom Autocorrect Implementation with Manticore [A Step by Step Guide] In this text I will explain what is spell correction in the area of search functionality, how it works in Google, Amazon and Pinterest and will demonstrate how to make your own implementation from the ground up using custom search engine Manticore Search. 28. Native Analytics On Elasticsearch With Knowi Table of Contents 29. Graph Databases: Full Detailed Review There are many ideas and considerations behind graph databases. This includes their use cases, advantages, and the trends behind this database model. There are also several real-world examples to dissect. 30. Docker Centralized Logging with ELK Stack [EXPLAINED] As your infrastructure grows, it becomes crucial to have robots and a reliable centralized logging system. Log centralization is becoming a key aspect of a variety of IT tasks and provides you with an overview of your entire system. 31. To be Relevant or not to be: a Search Story about Precision and Recall With the amount of data created growing exponentially each year and forecasted to reach 59 zettabytes in 2020 and more than 175 zettabytes by 2025, the importance of discovering and understanding this data will continue to be, even more than before, a decisive and competitive differentiator for many companies. 32. Elasticsearch in Java Spring Boot: Starter Pack In this article, I want to teach you how to connect Java Spring Boot 2 with Elasticsearch. We’ll learn how to create an API that’ll call Elasticsearch to produ 33. Aggregate Logs with Elasticsearch, Kibana, Logstash & Docker Improve logging in your microservices architecture to make tracking smoother with ELK Stack. 34. Let's Export Cloudwatch Logs to ELK Cloudwatch is an AWS service that allows storage and monitoring of your application logs from an array of AWS services. This can be really useful for creating alerts to notify developers when a certain threshold of errors has been hit, but sometimes we might need to deeply analyse our logs, not only to spot errors but to find insights into our application and improve performance. This is where an ELK (Elasticsearch, Logstash, Kibana) stack can really outperform Cloudwatch. ELK allows us to collate data from any source, in any format, and to analyse, search and visualise the data in real time. 35. Highlighting in Search Results In this tutorial you will learn how to highlight search results in Manticore Search. You can benefit from search results highlighting if you want to improve readability of search results in your application or a web site. 36. Doing First Steps with the Kubernetes Operator This article demonstrates how you can use the Operator Lifecycle Manager to deploy a Kubernetes Operator to your cluster. Then, you will use the Operator to spin up an Elastic Cloud on Kubernetes (ECK) cluster. 37. 3 Years After Forking Sphinx: A Brief Report on Manticore Search In May 2017 we made a fork of Sphinxsearch 2.3.2, which we called Manticore Search. Below you will find a brief report on Manticore Search as a fork of Sphinx and our achievements since then. 38. An Introduction to Elasticsearch: Lightning Fast Search Solutions If you're reading this blog, chances are you really interested in Elasticsearch and the solutions that it provides. This blog will introduce you to Elasticsearch and explain how to get started with implementing a fast search for your app in less than 10 minutes. Of course, we're not going to code up a full-blown production-ready search solution here. But, the below-mentioned concepts will help you get up to speed quickly. So, without further ado, let's start! 39. To be Relevant or not to be: a Search Story about Precision and Recall With the amount of data created growing exponentially each year and forecasted to reach 59 zettabytes in 2020 and more than 175 zettabytes by 2025, the importance of discovering and understanding this data will continue to be, even more than before, a decisive and competitive differentiator for many companies. 40. Fundamentals of Full-Text Operators and Basic Search In this tutorial, we will explore full-text search operators available in Manticore Search. Thank you for checking out the 40 most read stories about Elasticsearch on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

40 Stories To Learn About Elasticsearch

Back in the late 80s/early 90s, my dad acquired a PC which ran on the MS-DOS operating system. The darn thing was so expensive that I recall he had to pay for it in many multiple installments. The machine had around 10 or 20 of hard-drive (HD) space and 2 of RAM, which went from being loads of space and loads of memory to being insufficient really quickly. (The reason for should become clear as you read). Mega Something Mega Something Mega Something You probably guessed why it became insufficient: it was all due to games! To get games, I would either need to beg my parents to buy magazines that came with floppy disks or I would get them by copying from friends that also got them the same way. Mind that I had no concept of what piracy was and I had just started to learn English, so there was loads of trial and error to figure out how things worked. I recall getting angry when the games were hard to play (needing good English or being too complex to figure out) or when there were more tools than games on the floppies, which had 1.44 . Mega Something Little did I know then that those tools would eventually help. Megabytes Aren't Gigabytes, Working With What You Have Led by the need to extend the little amount of HD space I had, I began teaching myself how to partition files into chunks so they would fit in floppy disks so I could later uninstall something and re-install the large files back on disk. Although I had to do that often, I always took extra care to ensure everything was always working properly in case someone else needed to use the PC. As time passed, the rest of the family was not using the computer much, probably because I was always on it, so I started taking some liberties. Without fully understanding the risks, I eventually learned about executing a command called (later renamed Drivespace), whose dark magic made the HD suddenly have double the capacity (20 or 40 ). DoubleSpace Mega Something Oh, the Joy!!! I could not believe it! I think I’ve tried it a few times just to make sure my eyes were not deceiving me! And I recall screaming out of happiness. Everything was working fine and the HD had so much more capacity! But that didn't last. Eventually, the system began to have issues, the hard disk started to have bad sectors and there were other errors that came out of nowhere. With no one to ask for help, and as the magazines didn’t point to the command as the culprit, I had to spend several days in front of the computer trying out tools that came with the magazines to recover from Microsoft’s buggy Software to fix the HD. I also had no concept of what compression was, until I read about it in a magazine that came with this cool new utility that made life so much easier: Zip! It reduced my information transfer efforts, but it came a bit too late to save the HD. Those were good days spent on problem-solving, learning, and gaming. Man, did I squeeze the life out of that little hard drive! What is a Megabyte? When I learned it, we had: Bits; Bytes; Kilobytes; Megabytes. There were bigger units in 1991, but I didn't care much about: Gigabytes; Terabytes; Petabytes; Exabytes; Zettabytes; . Yottabytes A , and is, a 0 or a 1 and a has 8 . Historically a would have different values than 8, but I’ve learned it as 8 (dodged that one somewhat, perhaps a topic of interest for another day). Bit was Byte bits Byte And an 8 is called an . A term that unambiguously describes the number of bits in a . Bits Byte octet Byte A 1024 bytes and a HUGE, holding 1024 . Kilobyte was Megabyte was Kilobytes Life was simple, I had no network, all I cared about was having a way to express (the amount of information a device can hold). Or did I? You’ll find out. raw capacity As all my time was spent at the computer, and despite being very young, I became the local boffin (a person with knowledge or a skill considered to be complex) to those that knew me (not that many, I was still a kid). “Why is it 1024 and not 1000 ?” This was a question I would need to respond to often and to which I would happily respond, expert style: “I don’t know, it’s just like that!”. (Boffin no more, right?) Mega Something The fact is, as time passes, this question gets trickier to answer. And to make matters more interesting, the way I learned it is no longer (exactly) valid. Why? Well, we have more than one system. One is based on the while the other is based on the . power of 10 power of 2 Which one we use should depend on what we need to measure and according to who we want to measure, as some multiples of the units are formed with either (power of 10) while others use (power of 2). SI prefixes IEC binary prefixes Let’s look at the following table that splits (power of 10, 0 to 9) with (power of 2, 0 and 1): decimals binary As you can observe in the above table, the and the have the same value, but it changes from there onwards with substantial differences between the values, which identifies its relevance. Bit Byte The larger the unit, the larger the difference, and that’s what makes it important. But it can get confusing. For example, a MAC can be advertised in the Apple store as having a HD with 1 . If you would buy one, then you could go to “System Preferences” > “General” > “About” in Ventura, where you will be able to confirm that it does have the . Terabyte Terabyte However, if you would enquire in the command line using the “df -h” command, then you would get the response in , precisely 932 Gi (not GiB, which it abbreviates; perhaps the author will fix that in the next update). Gibibytes So hold your anger; now you know Apple measures in binary and did not steal almost 10% of the storage capacity you paid for. This happens in a lot of other circumstances too and is not restricted to Apple. So take care when you are buying something or need to be precise, particularly around old Software that may be referring to but naming them . As in those, the may still be equivalent to 1024 and not 1,000. Mebibytes Megabytes Megabyte Kilobytes You may be asking: Why do we need this? To me, it has to do with human needs, in this case, our need for the familiar and for consistency. In the case of they follow the same units as the metre (British English spelling). SI prefixes, If 1 Litre equals 1000 Millilitres and 1 Ton equals 1000 Kilos and a Metre equals 1000 Millimetres, why would a be 1024? Megabyte And this was the feedback I used to get when young, from adults that asked: “Why is it 1024?” In other news, since November 2022, and have been adopted by the and were added to the (the SI I’ve mentioned before), so now we have two more to know. Ronnabytes Quettabytes International Bureau of Weights and Measures International System of Units Yup, they have a lot of Bits! Let’s first observe how many Bits of the units refer to: some However, the precision (and confusion) does not end here. We also have the and . No, I’m not making those up. Megabit Mebibit Megabit and Mebibit, speed (time) matters When you want to refer to the amount of data transferred in a network (or telecommunication systems) as to figure out how fast it is, then you should use or which consider time. Megabits Mebibits, To transfer 1 in a second, you would need a network connection with a transfer rate of 8 per second. Megabyte Megabits Example 1: 1 (8,000,000 ) divided by 1 (1,000,000 ) results in (theoretically) 8 seconds of transfer speed. Megabyte Bits Megabit Bits Example 2: 1 (8,388,608 ) divided by 1 (1,048,576 ) results in the same 8 seconds. Mebibyte Bits Mebibit Bits The , as you probably guessed by now, measures in . Mebibit powers of 2 There you have it! You should now be able to scroll up and replace the with the desired precision. I hope you get them right. Mega Somethings

A Petabyte in Gigabytes: The Ultimate Guide to Units Of Information 

Un petabyte en gigabytes: la guía definitiva de unidades de información

Un pétaoctet en gigaoctets : le guide ultime des unités d'information

गीगाबाइट्स में एक पेटाबाइट: सूचना की इकाइयों के लिए अंतिम गाइड

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千兆字节中的拍字节：信息单位的终极指南

How big tech is taking our data and how we're going to get it back with perspectives on the current state of data from Seagate Technology co-founder Finis Conner When the first said that the world’s most valuable resource is data (not oil), few realized how true this statement actually was. But Google, Amazon, Microsoft, Apple, Oracle, Facebook—and many other firms—have known the tremendous value of data for decades. Economist These tech companies have spent the last 20-plus years collecting, harnessing, and controlling the near-limitless power of data. Not only has this skyrocketed the amount of global data to , but it has also allowed these firms to dominate the global digital advertising market, which is expected to reach in value in 2023 and billion in 2026. 175 zettabytes (175 trillion gigabytes) by 2025 $681 billion $876 But data isn’t just about advertising. When combined with sophisticated AI, data is also about large corporations being able to forecast future trends, gain competitive business advantages, steer public opinion, influence elections—even motivate political movements and protests. The question is, will the centralized control of data—and the power and wealth that comes from it—last forever? According to , co-founder of the world's leading hard drive manufacturer—Seagate Technologies—the days of centralized data control are numbered. Conner has seen data storage technology evolve from the initial 5-megabyte hard drives he brought to market in 1980 to the . From his 10,000-foot view, access to new blockchain-based encryption technology is about to empower ordinary citizens to recover, monetize, and democratize the value of the data that's rightfully theirs. Finis Conner 30+ terabyte drives coming out in 2023 Understanding the Value of Data To understand the value of data, look no further than the . It’s not a coincidence that four of the top five control some of the largest reserves of data in the world. These companies are earning hundreds of billions of dollars a year by aggressively acquiring, analyzing, leveraging, and selling the data they harvest from billions of global users (without paying a dime for it). most valuable companies in the world Image source: ﻿https://companiesmarketcap.com/ For example, Alphabet (Google) leverages the data it collects—from users of Search, Gmail, Maps, Youtube, and other services—to generate over $76 billion in revenue during the last quarter of 2022. Most of this revenue is from digital advertising. Facebook used the free data it harvests from users to generate revenue of . Again, over 97% of that revenue comes from digital advertising. $32 billion in Q4 2022 The hard drive companies Conner founded have helped to empower the advantages that these data-driven companies now enjoy. According to Conner, “Even companies without reputations for data are using it to rise above the competition. Tesla is an example of the absolute need to capture data from many points to compete in today’s marketplace. Tesla now enjoys its place as a top-10 global company by market cap due to the gathering and consumption of data from Tesla drivers. This data feeds the AI systems that are helping to constantly perfect its products.” Arguably, Elon Musk’s $44 billion purchase of Twitter in 2022 was also about data. Twitter and its partners have access to information feeds of hundreds of millions of global users—along with the constant stream of real-time data they create on their interactions with news, current events, and opinions. Tech Companies Earn Hundreds of Billions of Dollars From the Data of Unknowing Users Technology companies acquire data by providing free and low-cost digital platforms for individuals and businesses. As participants use and create content on these platforms, tech firms capture all of it. They learn about our likes, dislikes, conversations, desires, calendared plans, business objectives, customers, identities, geographical locations, social relationships, and political perspectives. Through AI-powered analysis, tech companies leverage this data to learn what motivates us to change our minds, alter our purchase decisions, and support certain causes. Unfortunately, the users of these platforms rarely understand that their extracted data is far more valuable than the free and low-cost tech services they receive. In an eye-opening , tech visionary Jaron Lanier offers a dramatic wake-up call. He explains how social media and other tech platforms make us dependent on their free services—for the sole purpose of capturing more data. He says that consumers and businesses are being short-changed. : video According to Lanier “This whole shadow economy that runs our world now and concentrates our wealth and reduces your future would not be possible without the data that’s coming from you and your friends. You are the fuel for it. You’re giving away all your data for free. You don’t know what data has been taken from you. You don’t know how it’s ultimately used. You don’t have any opportunity to take pride in it. You don’t have any opportunity to make it better. To you, it’s a mystery so you pretend it’s nothing. But it isn’t nothing.” “When you realize that data is the new oil. When you realize that this thing being taken from you, that you don’t think about, is the future economic value—is your future economic value [...] All of a sudden, I hope you can see that you’re giving away everything in exchange for almost nothing.” To understand what’s really going on, Conner says, “We should look at the amount of data being created, extracted, and saved—and the revenue being generated from it. I don’t think people have any idea what their data is worth, nor the tremendous value that their data is contributing to the data economy. The total amount of global data is set to nearly double in the next two years, from 97 zettabytes in 2022 to 175 zettabytes in 2025. Just in terms of digital advertising, that data is set to generate in revenue in 2023. And it’s all made possible by harvesting the data assets of people and businesses. However, none of that revenue is being shared with the ones who are actually creating it. That’s not fair.” $681 billion Image source: https://pixabay.com/photos/server-drive-bay-hard-drives-4393370/ As a “hard drive guy,” Conner has seen the need for storage capacity grow by orders of magnitude due to the massive increase in data created by individuals and companies. During the course of his career, data storage needs have gone from the first 5-megabyte Seagate disk drive that Apple adopted to markets that now require 30+ terabyte disks. Conner says, “Drive makers don’t increase storage capacity like this because they can. They do it because of the need to store more data. The fact that economists are projecting a cloud data storage market by 2029 and an digital advertising market by 2026 is the scale of the magnitude and the value of data. No wonder tech companies design elaborate dopamine-based strategies to addict more users and extract more data from them.” $376 billion $876 Consumers and Businesses Are Realizing That They’re Giving Away Their Most Valuable Asset for Nothing Gradually, the public is waking up. A recent poll from showed that: Pew Research Center “Some 81% of the public say that the potential risks they face because of data collection by companies outweigh the benefits, and 66% say the same about government data collection. At the same time, a majority of Americans (79%) report being concerned about the way their data is being used by companies or the government (64%). Most feel they have little or no control over how these entities use their personal information.” According to Conner, “People are asking what happens to their data. Where is it stored? Who benefits from it? And they’re realizing how valuable their data is. Soon, they’re going to want tools and platforms that help them take control and profit from it. Why can’t the creators of data benefit from the sale of it? I see a big opportunity in that, and we now have the technology to make it happen.” A Path to Recover, Own & Monetize Your Data Image source: https://pixabay.com/illustrations/globe-space-galaxy-data-technology-6858907/ A number of technology startups are already building platforms to help people and businesses secure and monetize their data. , a project that Conner is advising through , is an example. The goal of CIRUS Foundation is to return the control and monetization of data to the user. CIRUS Foundation Conner Group According to CIRUS co-founder Daniel Bland, “Today, only the largest technology companies in the world have the tools and capacity to unlock the inherent value of their data, but this is rapidly changing. CIRUS is leveraging newly available tools, such as blockchain technology, NFTs, and , to help individuals and businesses establish true ownership of their data and generate and start earning money from it.” Web 3.0-based platforms Conner adds that “CIRUS Foundation is democratizing the new data economy by moving toward decentralized networks that allow users to fully control their data and generate income from it.” Final Thoughts A lot more can be said in support of the need for democratizing access to the value of our data. However, it’s clear that a substantial opportunity exists to unlock this wealth by giving control and ownership of data back to the people and businesses that create it. Also, it seems fair that data and its value-generating potential should belong to those who generate it. Fortunately, —and other forward-thinking startups—are ready to serve the growing numbers of consumers and businesses that want to own, control, and profit from their data. According to Conner, “As more people wake up to the value of their data, disruptive solutions will be evolving to address this opportunity. In fact, we already have the technology to provide an immediate path for everyone to recover ownership and monetize their data. It’s time to start using it.” CIRUS Foundation Image sources: 1 , 2 , 3 , 4 Also published here .

Democratizing the New Data Economy in 2023: With Co-Founder of Seagate Technology, Finis Conner

There are four main data structures: structured data, unstructured data, semi-structured data, and metadata. Structured data is information that is formatted and organized for readability within relational databases. On the other hand, unstructured data’s format is undefined, not well-organized, and not usable by relational databases. In this article, I will explore structured vs. unstructured data, semi-structured data, how to convert unstructured data, and AI’s impact on data management solutions. What is structured data? Structured data is highly organized data formatted to integrate with a relational database easily. Structured data resides in tabular formats, such as SQL databases. As I mentioned previously, structured data is typically collected from spreadsheets and DBMS systems and is primarily, but not always, quantitative in nature. Because structured data complies with relational databases, it also easily integrates with AI technology, specifically machine learning. While structured data is widely regarded as the most sought-after and user-friendly data type, structured data is the least common data structure type. This is because structured data is collected from spreadsheets, relational database management systems (RDBMS), etc. If we were to think about where most of our data is collected, we would notice that public web sources and other more informal collection methods are the most common, even in the world of finance. However, while structured data accounts for only about , it is still extremely valuable, particularly to companies and investors. For example, secondary data usually falls under the 20% of cleaned and structured data. 20% of total global data Let’s take a closer look at structured data. How is structured data managed? The management of structured data was created by . IBM needed to develop a language that defined how each data point within a larger database relates to one another, creating the Structured Query Language, known more widely as SQL. IBM in the 1970s This language helped companies replace outdated paper-based business intelligence processes with digital processes. This digitalization has since enhanced data analysis and improved business operations by reducing costs and improving efficiency, among other benefits. Today we are seeing companies leverage the digitalization of such structured data for building AI-based tools and data-driven decision-making. “Structured data is well-organized and formatted, making it easy to find in relational databases. Unstructured data is difficult to capture, process, and evaluate since it lacks a predefined format or organization.” - Jason Mitchell, CTO of Smart Billions What is unstructured data? Unstructured data is not organized or formatted in a predefined data model. It is stored as media files or NoSQL databases. Typically qualitative in nature, unstructured data includes a variety of data types such as text, numbers, booleans, and enumerations. Because unstructured data is collected from a variety of sources, raw unstructured data in its unfiltered form is disorganized and complex. Therefore, it’s harder to analyze unstructured data. Despite its complexity, unstructured data, when integrated, organized, and analyzed properly, provides companies with high-quality qualitative data and business insights. This then raises the question, if unstructured data is so complicated, why is it so sought after? The answer is simple: unstructured data accounts for approximately 80% of data worldwide. More specifically, unstructured data is sourced from a variety of sources, including social media, documents, PDFs, audio files, video files, sensor data, and more. The variety of source types and loose formatting definitions require unstructured data to be processed and transformed for further analysis or integration. How is unstructured data managed? As I mentioned before, unstructured data accounts for the majority of data while also being the most complex, unorganized, and typically largest in size. However, because it is so valuable, data scientists have been looking for unstructured data management and storage solutions. Today, AI technology, automation, and cloud technology have been the key to such solutions. More specifically, unstructured data is stored using servers and cloud-based technology and is managed with natural language processing and data mining. Still, some companies refuse to manage unstructured data. They simply expand the storage space and fill it with unstructured data. However, unstructured data requires management in order to save storage space and have the ability to ingest other types of data. What is semi-structured data? Semi-structured data is a combination of structured and unstructured data. Semi-structured data has some organization and formatting but not enough to integrate into relational databases smoothly. Semi-structured data’s organization is minimal and is organized with tags, attributes, and other semantic markers. However, the resulting organization does not meet the standard of a relational database but can be altered in a way that can fit into easy-to-read tables and spreadsheets, making it a subset of unstructured data. How is semi-structured data managed? Similar to how unstructured data is managed and stored, semi-structured data is managed using servers, cloud-based technology, natural language processing, and text data mining. Ultimately, as we create more and more unstructured and semi-structured data, we will begin to see new trends in data management solutions. Structured vs. unstructured data: key differences Let’s take a closer look at the key differences and similarities between structured and unstructured data. Converting unstructured data As our global data sphere continues to grow, businesses will look for solutions to unlock the full potential of all data structures. Currently, data scientists can extract insights from unstructured and semi-structured data using a conversion process. Let’s take a quick look at this process. How to convert unstructured data 1. Analyze your data sources Before you begin the conversion process, you must identify and analyze what data you want to convert. This will require that you access a data lake, where raw unstructured data is stored, and decide what datasets to pull from. 2. Set clear goals and objectives This step can be done in tandem with the first step. Setting clear goals and objectives about what you want to extract from your data and how you might use and access it in the future will provide guidance for the rest of the process. 3. Evaluate and select the processing tools Once there are clear objectives, and your data sources have been analyzed, you can select which processing tools will work best. These processing tools include text mining, data extraction tools, and natural language processing tools. 4. Clear and tag the data Now that you have chosen the processing tool, you must clean and formulate the data according to the chosen tool’s guidelines. This might involve deleting extraneous symbols, whitespaces, and duplicate data. This step will help your processing tools understand the basic organization and entities within your data. 5. Data extraction: Text mining & Natural language processing (NLP) The final step involves running your data through the selected processing tool: text mining or NLP. Text mining involves sifting through textual data for significant words and phrases and extracting key features of each document or data file. NLP involves utilizing AI to sift through and decipher the natural languages (human languages) within textual data. Data structures and AI Our global data footprint is expanding at an exponential rate. According to a study by Seagate, the global data sphere is expected to reach , a massive growth from 45 zettabytes reported in 2019. 175 zettabytes by 2025 As our data footprint grows, companies are finding more ways to leverage this data for crucial business practices such as lead generation, market analysis, and business and investment intelligence, just to name a few. Likewise, as our data grows, the challenges surrounding the usability and hidden potential of data have become apparent. This is primarily due to the varying structures of data that are created and consequently collected. Ultimately, data scientists have turned to AI and machine learning for these solutions. Further, artificial intelligence utilizes both structured and unstructured data. More specifically, structured data utilizes machine learning, while unstructured data utilizes text mining and natural language processing (NLP), all of which are AI-based processes. Scientists have created AI-based tools that can extract valuable business insights from nearly all data structures. For example, companies can purchase semi-structured data from data providers, prepare said data for analysis or enrich their current datasets, and then extract valuable insights from the data with AI, analysis, or other automated data processing tools. “Structured data is utilized in machine learning and drives machine learning algorithms. Unstructured data is used in text mining and natural language processing.” - Veronica Miller, Cybersecurity Expert at VPN overview Top tools for structured and unstructured data analysis Structured data tools Best for content marketing teams that have little to no technical knowledge. Schema. It tests whether your URL or a code snippet has any issues. It’s very useful while installing schema into your website. Google’s Structured Data Testing Tool. It helps create data for articles, FAQs, breadcrumbs, and more. It also offers features such as SEO, SERP, and crawling, among others. Merkle. It also assists in creating data for articles, FAQs, and more. The RankRanger Structured Data Tool. Unstructured data tools An easy-to-use tool that allows for basic quantitative data analysis and visualization. Excel. A data visualization tool that allows making comprehensive data visuals for reporting. Tableu. An advanced data science platform that is capable of building predictive models and is compatible with large amounts of data. RapidMinder. An open-source data platform best for experienced IT professionals that are interested in creating specialized tools. KNIME. An advanced data visualization tool for business intelligence. Power BI. Wrapping up In all, understanding the various data structures is beneficial in informing companies about the importance of data management. In order to unlock the full potential of data, companies must maximize their data management and storage processes of both structured and unstructured data. As our global data sphere continues to grow at exponential rates, data scientists and companies will continue to develop management and storage for all types of data structures. Also published . here

Understanding the Main Differences between Structured and Unstructured Data

Companies in every industry and around the globe are harnessing the power of big data as the business landscape continues to evolve and grow increasingly competitive. Big data may seem like any other buzzword in business. Still, it’s important to understand how big data benefits a company and how it’s limited. If a company uses big data to its advantage, it can be a major boon for them and help them outperform its competitors. Below, take a deeper look at big data and some of its primary advantages and disadvantages. An Overview of Big Data As its name suggests, big data describes a collection of rapidly generated, large and complex unstructured and structured data. The amount of big data is growing quickly in the digital era. According to Statista, global data creation will . To put this figure into perspective, one zettabyte is worth one billion terabytes (TB) and one TB is equivalent to 1,024 gigabytes (GB). For context, the modern smartphone has a standard capacity of 32GB. increase to over 180 zettabytes Big data , including social media platforms, mobile phones, public records, service networks, search engines and many other connected devices. Businesses also have a plethora of sources to access to retrieve big data. comes from many sources Big data is so voluminous that traditional data processing software cannot manage them. However, companies can invest in various big data solutions, which allow virtually every department within a company to gain valuable insights from big data analysis. Advantages of Big Data While big data often provides companies with a gold mine of benefits, it can also pose potential challenges that could offset future gains. What are the main advantages of big data for enterprises? 1. Improves Decision-Making Organizations can gather valuable insights about their performance with the help of big data solutions. For example, big data analytics can help HR departments with recruitment and hiring processes. Poor hiring practices negatively impact a company because finding high-performing candidates takes time and resources. When a company uses big data in HR, it can make these processes more efficient and effective. 2. Reduces Costs Any professional knows how important it is for a company to keep costs down wherever and whenever possible. Here are a few ways companies are using : big data to reduce costs Facilitates targeted marketing campaigns to reach customers effectively Digitizes supply chains to improve efficiency and minimize costly disruptions Identifies instances of fraud to prevent loss Leveraging big data can provide more cost-cutting opportunities than just the above examples. Netflix, for example, uses big data to on customer retention alone. save around $1 billion annually 3. Increases Productivity IT specialists can increase their productivity levels by using big data solutions. Instead of manually sorting through all types of data from disparate sources, big data tools can automate the process and allow employees to focus on other meaningful tasks. When companies can analyze more data more quickly, it can speed up other business processes and increase productivity more broadly throughout the organization. 4. Enhances Customer Service Customer service plays an important role in determining a company’s reputation, customer loyalty and overall position in the marketplace. Big data analytics provide customer service departments , allowing managers to measure employee performance and overcome shortcomings. with myriad data-driven insights Because companies have access to a wealth of information through big data, it’s no surprise that many will use it to serve their customers better. Disadvantages of Big Data Below are some of the disadvantages of big data companies should understand. 1. Cybersecurity Risks As new technologies emerge in business, it’s understandable that there are some risks involved with adoption. Big data solutions are major targets for cybercriminals, meaning companies using these advanced analytics tools are exposed to more potential cybersecurity threats. Storing big data, particularly data of a sensitive nature, comes with inherent risks. Still, companies can implement various cybersecurity measures to protect their data. 2. Talent Gaps Data scientists and experts are in high demand as big data becomes more prevalent in business. These IT professionals are often paid very well and can significantly impact a company. However, there’s a lack of IT workers in the field capable of handling big data responsibilities. While having access to big data can benefit a company, it’s only useful if someone with a strong background in big data works with it. 3. Compliance Considerations Another disadvantage of big data is the compliance issues companies might deal with. Companies must ensure they’re meeting industry and federal regulatory requirements for the information they work with, including sensitive or personal customer data. Without a compliance officer, organizations would find it challenging to handle, store and leverage big data. For example, companies operating in the European Union must be aware of the General Data Protection Regulation (GDPR), focused on protecting consumers. a major data privacy regulation The role of big data is already playing a critical role in modern business. As time goes on, it’s expected that big data will continue on its growth journey and become a staple for just about every type of business, regardless of size or industry. Big Data: Playing an Increasingly Important Role in Business Although big data has pros and cons, company decision-makers need to understand the concept of big data and what it can do for their organization. Before using big data analytics, leaders must decide if the benefits outweigh the drawbacks. Expect more businesses to leverage big data and analytics tools in the future.

Advantages and Disadvantages of Big Data

Businesses have gone through a paradigm shift in the last few decades. The transition from paper to digital has resulted in the massive production of data. If reports were to be believed, will be produced by 2025. To understand this better, here’s simple math to follow - one zettabyte is roughly one billion terabytes. The rise in data presents an opportunity for enterprises to unlock new revenue streams, drive business growth, and gain a competitive advantage. However, the exponential rate with which data is churned out by enterprises, it becomes challenging for them to analyze the produced data, extract meaningful insights, and make informed business decisions. This is where the need for Business Intelligence and Data Analytics tools comes into action. 175 zettabytes of data Today, businesses are actively adopting BI and Analytics tools to analyze data and make informed decisions. According to a recent report, the global BI market was valued at 22.26 billion USD in 2020 and it reached 24.05 billion USD by the next year. Furthermore, it is estimated that by 2028, the global Business Intelligence market will reach 43.03 billion USD with a CAGR of 8.7% during the 2021-2028 period. This clearly indicates that BI and Data Analytics tools are here to stay since they empower the workforce to explore actionable insights hidden in data and make confident decisions. Among the sea of data visualization tools, Power BI is one such tool that has been garnering the attention of enterprises both large and small alike. Why so? Let’s find out. What is Power BI? Power BI is a robust yet interactive data visualization software solution engineered by the technology giant Microsoft with a key focus on BI and Analytics. In other words, it is a suite of data visualization tools, connectors, applications, and software services that work together to transform raw and unstructured data into interactive dashboards and rich visualizations. Since this BI and Analytics software converts raw data into visually immersive, coherent, and meaningful insights, enterprises across verticals are opting for Microsoft Power BI consulting services to spot hidden data patterns and trends, analyze data more efficiently, and make confident decisions in a quick turnaround time. Why is Power BI Immensely Popular Among Businesses? Microsoft Power BI is one of the popular data visualization software that has been around for over a decade and is backed by global enterprises including  KPMG, P&G, Toyota, Dell, Adobe, Astron Martin, Coca-Cola, Bentley, Carlsberg Group, and to name a few. What’s more interesting to note here is that Microsoft has been named as a leader for the 15th consecutive year in the Gartner® Magic Quadrant™ for Analytics and BI Platforms for 2022. With Power BI consulting & implementation, enterprises can establish a data-driven culture and empower team members to quickly analyze data and share actionable insights across the organization, or embed the same on the website or enterprise-grade application. 3 Key Reasons to Embrace Power BI in Your Business Listed below are the key reasons to adopt Power BI and become a data-driven organization. Easy to Implement and Use Power BI can be easily implemented without disrupting the established workflows. With the help of a Power BI implementation partner, enterprises can quickly complete the configuration process and start analyzing data. Unlike other data visualization software, Power BI comes with a simple and interactive interface that facilitates everyone at every level of your organization to discover powerful insights hidden in data without the need for knowing the popular programming languages or any other expertise.
Using the drag-and-drop functionality of Power BI, users can create interactive dashboards and data-driven reports in a quick turnaround time. Furthermore, the Q&A feature allows users to ask questions pertaining to their sales figures, profit margins, etc. and in response to the question, the Power BI tool will display a chart or graph that helps them visualize the data and make data-driven decisions. Connects With a Host of Data Sources With Power BI implementation, users can connect to a wide range of data sources ranging from Microsoft products such as Azure, Excel, and Dynamics 365 to SalesForce CRM, OneDrive, DropBox, and Google Analytics. Furthermore, it supports up to 70+ connectors, which in turn, enables enterprises to import data from data files (such as XML and JSON) and Amazon Redshift, Oracle database, MySQL database, SAP Hana, SQL Server database, Azure SQL Database, and to name a few. With these built-in connections, Power BI can import data from anywhere and perform advanced data analysis in a snap. Affordable Solution Another strong reason to adopt Power BI is that it is a relatively inexpensive and affordable solution for data visualization as compared to other Business Intelligence and Data Analytics tools. For instance, the Power BI Pro plan comes as little as 9.99 USD and is ideal for small and mid-sized businesses, while the Power BI Premium plan starts from $20 per user/month and is ideal for large-scale enterprises. Power BI also offers a free plan (i.e. Power BI desktop) that allows users to create interactive and visually-rich reports with ease. Summing Up Power BI has become the go-to solution for Business Intelligence and Data Analytics. If you also want to make the most of this data visualization software, you should get in touch with seasoned and seek end-to-end consultation from them. Power BI implementation consultants

Make Data-Driven Decisions With Power BI Consulting & Implementation

TL;DR: Insights, summarized: From 60% to 73% of all enterprise data goes unused for analytics. An average company due to missing out on data analytics opportunities. loses 12% of its revenue Combining and , businesses can tap into their data and glean insights that were previously unavailable. AI IoT solutions AIoT is a mix of artificial intelligence and the Internet of Things. Marrying both technologies allows creating more efficient solutions that drive higher ROI. The sectors where AIoT has found a wider use span healthcare, manufacturing, transportation, and other industries. What originally started as machine-to-machine communication limited almost exclusively to the telecom industry, the Internet of Things is now everywhere. According to Statista, the number of devices connected to the internet . will exceed 38 billion by 2025 The figure is arguable though, for it is hard to draw the line as to what exactly an IoT device is. So, other reports suggest more restrained numbers. Think: around 16 billion devices in use by 2025. The steep surge in the number of IoT devices will inevitably lead to an increase in the amounts of data collected. IDC reports that the volumes of IoT data generated globally will by 2025. And that’s where it gets problematic. The collected information needs to be processed and analyzed to drive value. However, most enterprises fail at putting data to use, with for analytics. reach 73 Zettabytes between 60% and 73% of it going unused The good news is that enterprises can turn more of the generated data into business insights by leveraging the combined power of artificial intelligence and the Internet of Things. In the article, we’ve covered everything you need to know about this potent mix, often referred to as the artificial intelligence of things, or AIoT. So, if you are considering jumping on an Internet of Things development bandwagon, carry on reading. What is AIoT exactly? An Artificial Intelligence of Things (AIoT) system is made up of two components: the Internet of Things (IoT) and Artificial Intelligence (AI). In this robust combination, the role of IoT is to accumulate structured and and enable the communication between the connected things and the user. unstructured data When amplified with AI — algorithms that can find complex interdependencies in huge amounts of data and describe, predict, as well as prescribe certain actions based on that — an IoT system gains human-like intelligence and can be applied to solve a wider variety of tasks. These could span “understanding” natural language, predicting users’ needs and adjusting a connected device’s behavior accordingly, and more. The AIoT market is currently on the rise. Recent research estimates that it will . And it’s perfectly clear why: AI adds value to IoT through improved decision-making, while IoT provides a platform for AI to drive value through connectivity and seamless data exchange. reach $102.2 billion by 2026 How does the Artificial Intelligence of Things work? AIoT systems may be implemented in two ways: As cloud-based systems As edge systems running on connected devices. The architecture of an AIoT system will vary depending on the implementation strategy. Cloud-based AIoT With the cloud-based approach, basic architecture of an AIoT solution looks like this: Device layer: various hardware devices (mobility, tags/beacons, sensors, health and fitness devices, vehicles, production equipment, embedded devices) Connectivity layer: field and cloud gateways Cloud layer: data storage, data processing (AI engine), data visualization, analytics, data access via API *: web portals and mobile applications* User communication layer Edge AIoT With edge analytics, the collected data is processed closer to the source — whether on connected devices or on field gateways. Collection terminal layer: Various hardware devices (mobility, tags/beacons, sensors, health and fitness devices, vehicles, production equipment, embedded devices) connected to the gateway over existing power lines Edge layer: facilities for data storage, data processing (AI engine), insights generation Edge-focused implementations do not exclude the presence of the cloud, though. Cloud-based data storage can, for example, be used for collecting metadata about the system’s performance or contextual information needed for training or retraining edge AI (think: a paradigm for crafting AI workflows that involves the cloud and the edge, the latter made of devices outside the cloud that are closer to physical things.) Top applications of AIoT in different sectors Driven by a number of factors, such as the availability of new software tools, the development of simplified AI solutions, the infusion of AI into , and advances in hardware upholding AI algorithms, the Artificial Intelligence of Things is creeping in many industries. Here’s a rundown of sectors that are already leveraging the opportunities provided by AIoT — with the most promising use cases spotlighted. legacy systems Healthcare Diagnostic assistance AIoT can help healthcare providers make more precise diagnostic decisions. The intelligent healthcare IoT solutions take in patient data from a variety of sources — from diagnostic equipment to wearables to electronic health records — and cross-analyze this data to assist doctors in correctly diagnosing a patient. are already surpassing human healthcare professionals in several diagnosis fields. Radiologists around the globe are relying on . AI-based medical solutions AI’s assistance for cancer screenings In a study published by Nature Medicine, in determining if patients had lung cancer. The algorithm that was trained on 42,000 patient scans from a National Institute of Health’s clinical trial data records, detected 5% more cancer cases than its human counterparts and reduced the number of false positives by 11%. It’s worth mentioning that false positives present a particular problem in diagnosing lung cancer: of 2,100 patients states a false positive rate of 97,5%. Thus, AI helps address one of crucial diagnosis problems. AI outperformed six radiologists JAMA Internal Medicine’s study AIoT systems perform equally well when diagnosing , . But the possibilities of smart, connected systems extend far beyond that. breast cancer skin diseases, and skin cancer Recent studies have shown that AI can , , , , and . detect rare hereditary diseases in children genetic diseases in infants cholesterol-raising genetic diseases neurodegenerative diseases predict the cognitive decline that leads to developing Alzheimer’s disease Improving treatment strategies and tracking rehabilitation process Following the same principle as in diagnosing patients, AIoT systems can help develop better treatment strategies and adjust them to the patient’s needs. Combining data from treatment protocols, patient’s history, and real-time patient information from connected equipment and wearables, smart algorithms can suggest dosage adjustments, exclude the possibility of a patient developing allergies, and avoid inappropriate or over-treatment. Some of the essential areas where AIoT is facilitating treatment span: More effective COVID-19 treatment By monitoring patients who have been diagnosed with COVID-19 via AIoT-powered wearables that record patients’ vital body signs, doctors could offer patients due suggestions, thus providing more effective outpatient care. Treating diseases accompanied by blood coagulation Connected coagulation devices help measure the pace at which blood clots form, thus helping patients make sure the measurements are within their treatment range and reducing the number of visits to the office as the measurements can be communicated to healthcare providers remotely and in real time. Better asthma and COPD management Chronic respiratory diseases (COPDs) affect around 500 million patients worldwide. To mitigate the severity of these conditions, patients have to stick to a thorough routine, and using inhalers is an essential part of it. Still, many patients fail to adhere to the recommended treatment plans. AIoT-enabled inhalers that are bound to a mobile app help avert that, recording the time, date, and location of each use. The collected data can be used to set up automatic reminders for the next usage, predict asthma attacks, and identify trigger factors. Optimized diabetes management In the US alone, 30 million people are affected by diabetes. And for them, regular glucose measurements have always been a concern. AIoT-enabled wireless implantable glucose meters alleviate those concerns by — and doctors — of changes in patients’ glucose levels. notifying patients Optimizing hospital workflows AIoT can transform the way hospitals are run, improving daily workflows in the following key areas: Reducing wait times Automated bed tracking systems powered by AIoT can help hospital workers admit emergency patients as quickly as possible by notifying them when a bed is free. The experience of early adopters, such as Mt. Sinai Medical Center in New York, proves that technology can help of emergency department patients. reduce wait times for 50% Identifying critical patients Identifying patients in need of immediate attention is critical in providing quality care. To make the right decision, doctors need to analyze large amounts of information, while being under significant pressure. AIoT can give the medical staff a helping hand in prioritizing their efforts. Connected systems may analyze patients’ vitals and alert doctors of patients whose condition is deteriorating. Several similar systems were tested out in intensive care units. For instance, the University of San Francisco piloted an AI solution that is able to detect early signs of sepsis, a deadly blood infection. The research results showed that patients whose treatments involved AI were ; and the death rate was reduced by 12%. 58% less likely to develop the infection Tracking medical equipment With AIoT-enabled equipment tracking, hospitals can reduce the risk of losing critical medical equipment and make more informed equipment management decisions, thus tapping into $12,000 of savings per bed annually. Critical medical equipment can be tracked via RFID or GPS systems in and out of the hospital, while medical and administrative staff can use web and mobile applications to quickly locate the needed equipment. Manufacturing Enabling predictive maintenance With machinery equipped with AIoT sensors measuring a variety of parameters, including temperature, pressure, vibration, rotation speed, and more, manufacturers can get real-time insights into the health of their assets and schedule maintenance according to the actual need. While basic analytics is often enough to detect equipment that is approaching a critical operating threshold, AI can predict anomalies in advance based on historical maintenance and repair data. As a result of predictive maintenance, according to a , manufacturers can improve equipment uptime by 9%, reduce costs by 12%, reduce safety risks by 14%, and extend the lifetime of their assets by 20%. PwC report Improving asset performance management With an AIoT system in place, manufacturers can get regular updates about how well their assets are performing and drill down the reasons for performance changes. The majority of IoT-based asset performance management systems allow getting automated alerts whenever a piece of equipment is deviating from the set KPIs. The AI engine, in turn, for performance deterioration, if there is any, and identify whether the measured KPIs are reasonable to track in each individual setting. Using performance management software, manufacturers optimize equipment utilization and improve the overall equipment effectiveness. helps dig into the reasons Boosting production planning with digital twins According to Gartner, digital twins can help manufacturers in production effectiveness. A digital copy of an asset, system, or process, an industrial, AIoT-enabled digital twin can help manufacturers gain an end-to-end visibility into the shop floor operations and help timely spot and even predict inefficiencies. gain a minimum of 10% improvement Manufacturing enterprises using digital twins state they , including an increase in reliability from 93% to 99.49% over two years, reducing receive maintenance by 40%, and saving $360,000 having predicted a power outage. could achieve lasting improvements Automating shop floor operations via industrial robotics Industrial robots have been a part of the shop floor for a long time. With getting more accessible, robots are becoming smarter and more independent. Equipped with sensors and relying on AI, industrial robotics is now capable of making well-informed production decisions on the go, thus increasing the effectiveness of manufacturing units. manufacturing IoT solutions Automotive and transportation Traffic management AIoT can be used to alleviate traffic congestion and improve transportation quality. Taipei City, for instance, tapped into AIoT to monitor and control signaling equipment at 25 conjunctions. In this system, smart sensors and video cameras gathered real-time data on traffic and human flow and road occupancy, while AI algorithms analyzed this data and applied appropriate control logic. The approach helped the city administration optimize traffic flow and ensure a safe and smooth driving experience. Self-driving vehicles Self-driving vehicles and advanced driver assistance systems (ADAS) are notable examples of interpreting and acting on real-time IoT data. AI algorithms Self-driving, or autonomous cars create a map of their surroundings based on the data from a variety of sensors. Radar sensors, for example, monitor the position of nearby vehicles; video cameras detect traffic lights, road signs, other vehicles, and pedestrians; lidar sensors measure distances, detect road edges, and identify lane markings. AI software then processes the sensor data, plots an optimal path, and sends instructions to the car’s actuators, which control acceleration, braking, and steering. Hard-coded rules, obstacle avoidance algorithms, predictive modeling, and help the software follow traffic rules and navigate obstacles. object recognition Key AIoT implementation challenges, and how to solve them Out of all IoT projects implemented globally, , with 30% of them failing as early as in the Proof of Concept phase. To avoid directing investments into initiatives that are doomed to collapse, companies testing out AIoT waters should be aware of the common challenges that may hinder their AIoT implementations. The obstacles businesses encounter most often span: 76% fail . Kicking off AIoT projects, organizations may get caught up in the novelty and fail to assess the feasibility of their ideas. That, in turn, could result in uncontrolled cost creeps at the latter stages of development and, ultimately, dissatisfied stakeholders. To avoid that, we advise starting your AIoT project with a discovery phase, wherein ideas can be vetted and weighed against the set business goals, customer expectations, and organizational capabilities. Embarking on an AIoT journey with no clear goal in mind . As said earlier, AIoT solutions can be implemented as cloud, edge, or hybrid systems. While drafting the implementation strategy, carefully weigh out bandwidth, latency, and speed requirements for the future solution and map them against the set costs. The rule of thumb is going for edge deployments for time-critical systems spanning a large number of devices and relying on the cloud in case minimal latency and high bandwidth are less critical. Struggling to choose an optimal implementation strategy . AIoT projects require a long-term commitment. Depending on a particular use case, the implementation process can take several months up to several years. With the technology landscape changing quickly, there’s a risk of a solution becoming outdated by the time it’s fully operational, as well as losing control over the implementation costs. To prevent that, businesses need to be agile enough to be able to introduce changes along the way. Slow deployment cycles with costs that are difficult to estimate . Depending on the scale of your future solution and the industry you operate in, you might need to connect highly heterogeneous legacy equipment to AIoT. Often a difficult task to accomplish, it needs planning and understanding the available options. For example, you can choose to attach sensors to the legacy machines, connect them via gateways, or even replace them altogether. No matter the approach, make sure to draft the feasible digitization scenarios early on. The need to connect highly heterogeneous and complex systems . To generate reliable insights, AI algorithms need to be trained on vast amounts of data. If it is not available in sufficient amounts (or is available but cannot be used due to privacy reasons), you’ll have to use other strategies to compensate for the lack of data. Common ways include transfer learning (think: using an already trained neural network that solves a similar problem), data augmentation (modifying the existing samples to get new data entries), or resorting to synthetic data. Not having enough data for training AI algorithms . The performance of AIoT systems depends on an array of factors, including hardware capabilities, data load, system architecture, the approach to implementation, and more. To avoid performance issues in operation, plan out potential data loads in advance and tune the implementation strategy accordingly. Struggling to achieve AIoT system’s sufficient performance . Many AIoT projects fail because the security of data, devices, servers, and communication networks was not taken into account during the planning phase. If you deal with highly sensitive data, consider hybrid deployments, where the data is processed closer to the source, so the risk of it being compromised during transit or in the cloud is minimized. Addressing software and firmware vulnerabilities If you have unanswered questions about the Artificial Intelligence of Things or are already considering embarking on an AIoT implementation journey, . contact our experts

AI Meets IoT: What is the Artificial Intelligence of Things

Artificial intelligence and blockchain are trending technologies that are improving business models and transforming entire industries. Deloitte refers to them as the most exciting trends of 2022, and Gartner their rapid development for decades to come. predicts Don Tapscott, Blockchain Research Institute founder, talks about the possible merging of blockchain, AI, and the Internet in a trivergence. This trivergence will be the basis of the second era of the digital age, where blockchain will record and protect data as AI will analyze it. Let's take a look at how blockchain and AI can work together. An Excursion into AI and Blockchain Technology Many people consider distributed ledger technology, or blockchain, to be synonymous with cryptocurrency. This isn't the case, as cryptocurrency - while being the most popular - is just one of the many ways that blockchain technology can be harnessed. A blockchain database is distributed over blocks linked together in a chain. Entries in a block are hashed so that no one can change or delete the information. For transactions to be carried out honestly, a consensus mechanism works in the network when blockchain participants confirm or reject unauthorized actions. These features are adopted by industries to prevent fraud, protect information, and/or manage business-critical transactions. AI, on the other hand, is a set of deep and machine learning algorithms that mimic human thought. AI processes and arranges volumes of data that are beyond the extent of human capabilities. Furthermore, AI takes the burden off people by automating routine manual operations. For enterprises, it is becoming a business intelligence tool or a mechanism for protecting against cyber-attacks. AI can be applied to everything from writing articles for the Associated Press to early detection of diseases. Since AI and blockchain are not related in any way, how then can they work together? Understanding the Connection between Blockchain and AI Despite the dissimilarity between the two technologies, they can be used to complement each other and eliminate each other's shortcomings. Thus, blockchain will create more trust in AI platforms, maintaining security and privacy. AI will strengthen the protection of transactions, and contribute to the monetization of information. Blockchain will also clarify the work of AI by recording in detail the data on which decisions made by smart algorithms are based. AI, in turn, can support the scalability of blockchain applications by automating the management procedures of a decentralized network. It allows businesses to collect, analyze and apply the data received, while blockchain allows them to check, record, and perform certain actions with them. AI and blockchain are like the yin-yang of the digital world, providing businesses with the missing harmony. , CEO of Walnut, these technologies enhance each other, and entrepreneurs should explore their implementation options to gain a deeper understanding of business. According to Yoav Vilner 4 Instances of a Beneficial Relationship between Blockchain and AI The convergence of blockchain and AI opens up many new business opportunities. Let's look at promising use cases of this powerful combination of technologies. 1. Enhanced data protection mechanism Blockchain is considered synonymous with security because every block in a chain is encrypted with a hash algorithm. It is almost impossible to decrypt the data because a unique key is created for each block. In addition, this key changes when a new block is added to the chain. A user has a personal key to access information and a public key to conduct transactions. Despite such multi-layered protection, vulnerabilities can be found in blockchain-related applications. Take, for example, the DAO , when the company lost over 3.6 million ether, or the on the Bitfinex cryptocurrency exchange when an amount of 119,000 Bitcoin was stolen. hack attack AI strengthens the existing blockchain security mechanism, bringing the risks of attacks to a minimum. A smart algorithm analyzes anomalies in the blockchain, and in the future, it will learn how to work with encrypted data. It will analyze arrays of protected data and find potential bottlenecks so that developers can deal with them. For example, say a person invests a fixed amount of money in EFT every month. Suddenly, an investment exceeds the usual sum by 10 times. AI evaluates such a transaction as suspicious, and an anti-fraud mechanism is triggered to stop anomalous activities in time. 2. Validation of decision-making process by AI algorithm Explainability is one of the unresolved issues that is hindering the widespread adoption of AI. It is difficult for people to rely on AI when they do not understand why a certain solution is proposed. For greater confidence, people need the logical validity of the proposals put forward by AI. , a method elaborated by scientists to increase the transparency of AI, and attention techniques are still far from ideal and are still being developed. Blockchain claims to be the third and more justified solution to the problem of explainability of AI. LIME Blockchain can make the secret and the incomprehensible in the work of AI transparent and explainable. The distributed database stores each decision made by AI in blocks so that the user can view and analyze it. At the same time, decentralization and enhanced encryption of the registry ensures that information is protected from unauthorized access. When people begin to understand how the decision-making mechanism of AI works, they will trust the technology more. This, in turn, will accelerate the pace of implementation of AI in healthcare, fintech, logistics, and other areas of human activity. Such a move towards transparency would make AI-powered blockchain medical applications a common practice. Logistics companies will launch blockchain and AI-based infrastructure to monitor the supply chain in real-time. Cadastral companies, like Sweden's , will use blockchain and AI to store and track documents for property sales. Lantmäteriet Soon, blockchain and AI might be able to comply with the conditions of the GDPR specified in Article 22 and WP29. A person will receive an explanation of decisions and with it the right to accept or challenge them to control AI. 3. Improved data control and monetization The volume of data generated by mankind is growing rapidly and, , will increase from 64 zettabytes in 2020 to 180 zettabytes in 2025. Such a large stream of data needs to be processed, analyzed, and stored securely, providing access only to the owners. according to Statista experts The data that businesses collect may relate to individual customers, suppliers, and products. They also need to be correctly extracted and researched to be used for critical business processes, key business forecasts, and fundamental decisions for development. AI takes over this function by working with data in a decentralized secure network. At the same time, the convergence of AI and blockchain makes possible a new type of income - the monetization of personal data. An owner can store personal information in a decentralized network and sell it to interested parties: research organizations, medical institutions, and other companies. For a certain amount, a user can open temporary access to personal data. Information-selling platforms like are creating a marketplace for data that can be used for business purposes. Hu-manity.co Each organization will be able to leverage the convergence of AI and blockchain in different ways, depending on the goals and type of underlying data. Firms that are first to adopt these technologies will see immediate results and gain an edge over companies that are just starting to introduce innovations. 4. Reducing power consumption when mining To keep the Internet running, servers need to be operating 24 hours a day, 365 days a year. They need a constant flow of electricity - about 1% of the world's supply, and most of it goes to cool data centers. that maintaining the Internet can cause 0.3% of global CO2 emissions. Planetly estimates Tech giants began to look for ways to reduce the negative impact on the environment. So, Google has been using the DeepMind algorithm to manage the data center for more than 5 years. Trained on an array of historical data, the neural network “knows” how to rationally use the cooling system. As a result, Google managed to energy consumption by 40%. reduce A similar control mechanism can be introduced into the mining process to cool large data centers. AI in this case will not only reduce environmental pollution but also lead to lower prices for mining hardware. Conclusion The four AI and blockchain use cases mentioned above are not just theories. Organizations possible ways to apply technologies in a single project. As smart algorithms and decentralized ledgers improve, it will be possible to implement profitable business solutions. are testing AI needs blockchain to show how a smart algorithm works. Blockchain benefits from stronger AI defenses, better smart contracts, and lower power consumption. Soon, we will surely witness discoveries related to the joint use of AI and blockchain. The reality that seemed fantasy is approaching.

Blockchain - AI Symbiosis and the Future of the Digital Age

Data is the world’s most valuable resource. However, like any other resource, it doesn’t provide any value on its own. Businesses have to use it properly to benefit from it, and that means choosing the best way to store it. The world creates, captures, copies, and consumes a year. Handling that much data, especially with the necessary organization for in-depth analytics, is a complicated task. Data scientists have turned to data warehouses and lakes to help, but now a new model has emerged: the data lakehouse. dozens of zettabytes of data A Brief History of Data Architecture To understand data lakehouses, it helps to understand the models that came before them. As companies’ data grew in volume and complexity, they quickly realized they needed to organize it to find what they needed faster and more accurately. Data warehouses were the solution. Warehouses standardize and format incoming data to store it in a consolidated, structured environment. This eliminates redundancies, makes it easier to find and analyze data, and improves data insights by offering a more reliable single source of truth. As more complex processes like machine learning gained steam, warehouses’ limitations became more concerning. Data lakes in 2008, offering a more flexible option. Unlike warehouses, lakes store data in its original, unstructured format, making it cheaper and easier to scale up and store vast amounts of information. However, this flexibility and affordability come at the cost of losing the speed and reliability of warehouses. emerged shortly after Apache Hadoop premiered The Data Lakehouse Data lakehouses offer the best of both worlds. They aim to offer the flexibility, cost-efficiency, and support for complex analysis processes of data lakes while maintaining warehouses’ organizational benefits. Lakehouses more closely resemble data lakes at first. Like lakes, they take data in its raw format, storing structured, semi-structured, and unstructured information in a single, vast repository. Unlike data lakes, lakehouses then use a single storage layer that offers indexing, caching, metadata, and compaction support that data passes through before going to specific end-uses. When businesses need to use their data, it flows from the storage layer, through the compute layer for organization, and then through open APIs to meet various use cases. As a result, they meet the need for both flexibility and organization. Benefits of Data Lakehouses Right now, more than sits in data lakes. However, that could change soon as more organizations realize the advantages of data lakehouses over these older architectures. Here are some of the most significant of these benefits. 90% of enterprise data Cost-Effectiveness Data warehouses can , as it takes considerable time and computing resources to organize large data sets before storing them. Lakes offer a more affordable alternative, but they lose warehouses’ visibility and reliability. Lakehouses stand as the most cost-effective solution. quickly become expensive to implement Since the data remains in a lake during storage, lakehouses offer the low-cost scalability of conventional lakes. When businesses need their data, the lakehouse will then run it through organizational tools to provide the necessary visibility and consistency. Organizations no longer have to sacrifice performance for affordability. Because of this organizational layer, lakehouses cost more to implement than lakes. Still, they’re not as expensive as warehouses and their reliability can lead to process improvements that make up for the extra expenses. All in all, they offer the best balance between performance and cost. Optimized Performance Data lakehouses also benefit from the performance advantages of both lakes and warehouses. Warehouses enable far faster and, at times, more accurate analysis because of their standardization and organization. Lakes, on the other hand, enable more advanced analytics processes. Data lakehouses provide for both and include several helpful optimization features. The compute layer offers support like caching, data skipping, and clustering to help refine data as needed for the specific use case at hand. Since data doesn’t go through this organizational layer until businesses use it, methods can match each end-use. Many organizations try to balance the benefits of lakes and warehouses by using a mix of both, but this creates redundancies. Lakehouses provide a combination of their benefits while keeping a single repository, eliminating redundancy. As a result, they outperform hybrid structures, too. Flexibility Similarly, data lakehouses offer a more flexible approach to data architecture. Lakehouses use open formats like Parquet and ORC, as well as open APIs using languages like SQL, R, and Python. This makes them interoperable with many other apps, integrations, and processes. Warehouses are ideal for business intelligence (BI) applications, while lakes are better-suited for direct access to large datasets for processes like machine learning. Since lakehouses feature a data lake and an organizational layer, they can meet the specific needs of both. Regardless of what types of applications businesses run their data through, the lakehouse can support it. In a recent study, believed lakehouses offered the best all-around solution after experts weighed in on the pros and cons of each model. Interestingly, just 37% felt that way before the discussion. Once the way lakehouses work becomes clear, it becomes more apparent that they’re the most flexible data storage solution. 68% of survey respondents Easier Security and Governance Security and regulatory compliance and growing concerns for any data operation within a business. In fact, security and visibility account for the that companies face today. Data lakehouses’ organization and flexibility make it easier to adapt to these changing security and compliance needs. four largest barriers to cloud migration Lakehouses’ compute layer can apply auditing and security mechanisms across an entire data lake, meeting stringent needs despite rising unstructured data. Similarly, their support for Atomicity, Consistency, Reliability, and Durability (ACID) transactions ensures data integrity to meet regulatory requirements. Since data lakehouses offer more visibility and control for vast repositories, they make it easier to find and fix anomalies. The compute layer also makes it harder for poor-quality or poisoned data to influence end-uses. Data Architecture Is Evolving Data lakes have effectively replaced warehouses, but these architectures are becoming outdated, too. Just as lakes helped meet evolving flexibility and cost needs, lakehouses will help meet modern businesses’ security, control, and reliability requirements. Data lakehouses are still new, so it will likely take some time before they see widespread adoption. Despite their novelty, early signs are promising. These new models could provide businesses with the best of both worlds for data storage.

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