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How to Generate Large Datasets in .NET for Excel With OpenXML

কিভাবে OpenXML এর সাথে Excel এর জন্য .NET-এ বড় ডেটাসেট তৈরি করবেন

So generieren Sie mit OpenXML große Datensätze in .NET für Excel

Cómo generar grandes conjuntos de datos en .NET para Excel con OpenXML

Comment générer de grands ensembles de données dans .NET pour Excel avec OpenXML

OpenXML के साथ Excel के लिए .NET में बड़े डेटासेट कैसे उत्पन्न करें

OpenXML を使用して .NET で Excel 用の大規模なデータセットを生成する方法

OpenXML을 사용하여 Excel용 .NET에서 대규모 데이터 세트를 생성하는 방법

Como gerar grandes conjuntos de dados em .NET para Excel com OpenXML

Как генерировать большие наборы данных в .NET для Excel с помощью OpenXML

OpenXML ile Excel için .NET'te Büyük Veri Kümeleri Nasıl Oluşturulur

Cách tạo bộ dữ liệu lớn trong .NET cho Excel bằng OpenXML

如何使用 OpenXML 在 .NET 中为 Excel 生成大型数据集

Telegram: Crypto Island's Bridge to the Mainland

টেলিগ্রাম: ক্রিপ্টো দ্বীপের মূল ভূখণ্ডের সেতু

Telegram: Die Brücke der Kryptoinsel zum Festland

Telegram: el puente de Crypto Island hacia el continente

Télégramme : le pont de Crypto Island vers le continent

टेलीग्राम: क्रिप्टो द्वीप का मुख्य भूमि से पुल

Telegram: クリプト島と本土を結ぶ橋

텔레그램: 크립토 아일랜드에서 본토로 연결되는 다리

Telegram: a ponte da Crypto Island para o continente

Telegram: мост Крипто-острова на материк

Telegram: Kripto Adası'nın anakaraya olan köprüsü

Telegram: Cầu nối đảo Crypto vào đất liền

Telegram：加密岛通往大陆的桥梁

Axion Processors: Google's First Arm-based CPUs

"AI can enhance data collection, analysis, prediction, and evaluation processes"

"এআই ডেটা সংগ্রহ, বিশ্লেষণ, ভবিষ্যদ্বাণী এবং মূল্যায়ন প্রক্রিয়াগুলিকে উন্নত করতে পারে"

„KI kann die Prozesse der Datenerfassung, -analyse, -vorhersage und -auswertung verbessern“

"La IA puede mejorar los procesos de recopilación, análisis, predicción y evaluación de datos"

"L'IA peut améliorer les processus de collecte, d'analyse, de prédiction et d'évaluation de données"

"एआई डेटा संग्रह, विश्लेषण, भविष्यवाणी और मूल्यांकन प्रक्रियाओं को बढ़ा सकता है"

「AIはデータの収集、分析、予測、評価のプロセスを強化できます」

"AI는 데이터 수집, 분석, 예측, 평가 프로세스를 향상시킬 수 있습니다"

"A IA pode aprimorar os processos de coleta, análise, previsão e avaliação de dados"

«ИИ может улучшить процессы сбора, анализа, прогнозирования и оценки данных»

"Yapay zeka veri toplama, analiz, tahmin ve değerlendirme süreçlerini geliştirebilir"

"AI có thể tăng cường quá trình thu thập, phân tích, dự đoán và đánh giá dữ liệu"

“人工智能可以增强数据收集、分析、预测和评估过程”

Startups of The Year 2023 Winners: North America 

Serverless NestJS Automation on AWS Lambda

Converting Excel or CSV files into Java objects (POJOs) and vice versa can be a complex process, but with the right tools and techniques, it becomes much more manageable. In this guide, we’ll explore a powerful library that leverages Java reflection. Java First, we add the dependency to Maven. <dependency>
  <groupId>com.adnanebk</groupId>
  <artifactId>excel-csv-converter</artifactId>
  <version>0.0.1-SNAPSHOT</version>
</dependency> Understanding the POJO Class Before we dive into the library, let’s take a close look at a sample Java class that serves as our data model: @Data
@Builder
@AllArgsConstructor
@NoArgsConstructor
@SheetDefinition(datePattern = "dd/MM/yyyy")
public class Product {

    @CellDefinition(0)
    private String name;

    @CellDefinition(1)
    private long price;

    @CellDefinition(2)
    @CellBoolean(trueValue = "yes",falseValue = "no")
    private boolean active;

    @CellDefinition(value = 3, title = "Promo price")
    private double promoPrice;

    // Additional fields...

    @CellEnum(enumsMapperMethod = "categoryMap")
    @CellDefinition(10)
    private Category category;

    @CellDefinition(11)
    private LocalDateTime localDateTime;

    private Map<Category,String> categoryMap(){
        return Map.of(Category.A,"Formatted A",
                      Category.B,"Formatted B");
    }
} This class is annotated with various annotations that play a crucial role in the conversion process. Each field is annotated with , indicating its position in the . Product @CellDefinition Excel or CSV file we can also define the title of the of the cell, by default, it will convert the camel case name of the field to a name with spaces (ex: firstName=>First name) The annotation provides additional information like date formatting patterns that will be used during the conversion of date field types. @SheetDefinition The Enum Annotation: @CellEnum(enumsMapperMethod = “categoryMap”) In the class, we make use of the annotation in the enum Category field. The enumsMapperMethod argument allows us to define a method name; this method should return a map that defines the mapping (conversions) between the enum constants and the formatted values in the Excel/CSV cells (by default, the enum constants will be used), note that the method name must much the enumsMapperMethod argument value. Product @CellEnum The Boolean Annotation: @CellBoolean(trueValue = “yes”,falseValue = “no”) we make use of the @CellBoolean annotation in the boolean active field, it has two arguments that represent the formatting values we want to use in the Excel/CSV fields. Now, let’s introduce an updated version of our POJO class, : ProductV2 @Data
@Builder
@AllArgsConstructor
@NoArgsConstructor
@SheetDefinition(includeAllFields = true,titles={"Name","Category","Date"})
public class ProductV2 {

    private String name;

    // Additional fields...

    private Category category;

    @IgnoreCell
    private LocalDateTime localDateTime;
} Converting Excel files to POJOs becomes even simpler with the annotation provided in the class to map the fields to corresponding cells in the Excel file. @SheetDefinition When the includeAllFields argument is set to true, the fields are automatically included and mapped in the cells based on their declared order and ignore fields that are annotated with @IgnoreCell annotation. We can define the titles in the titles argument with the condition that they must be in the same order as the fields. Converting Excel/CSV to POJOs and vice versa @RestController
@RequestMapping("excel/products")
public class ExcelFieldsController {
    private final ExcelHelper<Product> excelHelper = ExcelHelper.create(Product.class);

    @GetMapping
    public List<Product> excelToProducts(@RequestBody MultipartFile file){
        return excelHelper.toStream(file.getInputStream()).toList();
    }

       @GetMapping("/download")
    public ResponseEntity<InputStreamResource>
    downloadExcelFromProducts() {
        String filename = "products-" + LocalDate.now() + ".xlsx";
        InputStreamResource file = new InputStreamResource(excelHelper.toExcel(getProducts()));
        return ResponseEntity.ok()
                .header(HttpHeaders.CONTENT_DISPOSITION, "attachment; filename=" + filename)
                .contentType(MediaType.parseMediaType("application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"))
                .body(file);
    }
} the same is applicable for converting CSV files, except we need to define the delimiter that will be used private final CsvHelper<ProductV2> csvHelper = CsvHelper.create(ProductV2.class,";"); The Class: Dynamic Class Examination ReflectionUtil The ReflectionUtil class serves as the backbone of this Java library, facilitating dynamic class examination and manipulation through the power of Java reflection. One noteworthy feature of the ReflectionUtil class is the optimization applied to enhance performance. During initialization, all getters, setters, and fields are eagerly loaded and encapsulated in the SheetField record. This deliberate action minimizes the need for reflection lookups in subsequent operations and boosts overall efficiency. Record Overview SheetField The Field record is a fundamental component of the library, designed to encapsulate information about the field of a class. public record SheetField<T>(String typeName, String title, Function<T,Object> getter, BiConsumer<T,Object> setter, int cellIndex) Key methods : : Retrieves the value of the field from an object using its getter method. If the field is an enum, it provides formatted values based on defined enum mappings. public Object getValue(T obj) : Sets the value of the field in an object using its setter method. It handles enum values and ensures proper conversion. public void setValue(T obj, Object value) Conclusion By leveraging this custom library, developers can significantly simplify the process of converting Excel and CSV files to . The integration of Java reflection, along with thoughtful design considerations, empowers dynamic mapping, making it a valuable tool for data processing tasks. POJOs in Java GitHub repo https://github.com/adnanebk/excel-pojo-converter

Simplify Data Conversion: Excel and CSV to Java Objects with Reflection

This is the first article in the series on programmatic advertising. In this series, I will introduce the reader to the world of mathematical problems and machine learning challenges arising in the world of online advertising. We will start with a simple introduction and brief overview of the ecosystem and business models in general, slowly going into more and more mathematical detail. The first collection of articles will provide a short introduction to the programmatic advertising ecosystem and will only briefly mention arising challenges and mathematical problems that will be covered in greater detail in the later articles. The main players What happens when you open your favorite news website and see another shoe ad? Most probably, the ad was selected automatically through programmatic advertising, which we will refer to as , or RTB. This means that multiple computers make complicated computations and exchange a lot of HTTP requests in real time just to select and show the ad that is thought of as the best for you right now. Well, best for everyone, including you to some extent. Let’s look at the whole ecosystem and companies involved. Real-Time Bidding The user is you. You open your usual news website to catch up with what is going on in the world. In a couple of places within the news article there happen to be some ads. Most of the time you ignore them, but once in a while ads actually promote something interesting for you, and you might click on them, or even buy something on the advertiser's website afterwards. The User Supply-side: The Publisher Your favorite news website runs a business model that is called . When you open the news page in the browser, your smartphone sends a request for a web page to the website. While figuring out what kind of news to show to you, the website is also finding the options of ads to display — and sends a reply to your smartphone with a formatted web page containing news and ads personalized just for you. Usually, the server is expected to return a fully formatted web page with all the content and ads in 1 second. In order to choose the ad, within this 1 second the website usually does the following: groups information about: Publisher the page you are about to see (URL, maybe some keywords and classification tags), the ad space (size of the ad to be shown, its position within the page) and about you (your browser, IP address, and anything else it is able to find) Then it sends this information to the ad exchange, asking: “Which ad should I show of this size, on this page for this user?” — we will call this or . Ad exchange replies with the ad, which the website puts into the page and sends the page back to you. Bid Request Ad Request Supply-Side: SSP For the sake of this article, we will treat the ad exchange the same as the , or , even though it is a bit more complicated in reality. Thus, the main task of ad exchanges is running ad auctions. When SSP receives a bid request for an ad from a Publisher with information about a page, ad space and user, it does the following steps: Supply-Side Platform SSP First, it forwards the ad request from Publisher to all the interested ad agencies and collects their replies, which usually contain information about the ad to be shown (a picture and a link where user should land after clicking on the ad) and the bid price that ad agency is willing to pay to show this ad to this user in this page at the moment. We will call these . Bid Replies Then, SSP runs an auction — basically, selects the ad that bids the most, sends the winning ad to the publisher to be shown to the user, and charges the agency for the corresponding price. If SSP is playing a first-price auction (the clearing price is the winner’s bid), then the result of the auction can be described in simple formulas: winner ad = argmax bid_i clearing price = max bid_i Some share of the money SSP keeps for its services, another share goes to Publisher. Demand-Side: DSP Ad agencies that listen to ad exchanges’ auction announcements and reply with ads and bids are called , or . Sometimes, they are just called since their main task is to bid in the ad auction, preferably with reasonable bid amounts. For the sake of simplicity, let’s consider that DSP works with only one advertiser that has only one ad, so it will be sending the same ad for participation in all SSP’s auctions. Then DSP only needs to compute the right bid for each particular auction. Usually, DSP’s auction bid equals the amount of revenue DSP is expecting to get for showing the ad on average. For example, if an advertiser promises to pay DSP $20 for each purchase of a pair of shoes, DSP predicts the probability that this user, seeing this ad on this page, will buy a pair of shoes afterwards — and computes the auction bid as a product of $20×purchase probability. Basically, DSP’s auction bid is a mathematical expectation of DSP’s revenue from showing the ad: Demand-Side Platforms DSPs bidders bid = advertiser_payout = CPA * p(purchase | user, ad, page), E Where is a mathematical expectation, CPA is how much the advertiser will pay DSP for each desired action, and p is the probability of the user performing the action if they see this ad on this page at the moment. However, DSP may work with thousands of advertisers and may have millions of ads that need to be ranked for each auction. There might be hundreds of thousands of auctions happening every second. The predicted probability of purchase should be computed quickly and efficiently but should be precise enough. Thus, to simplify even more, DSP is paying its own money for showing all the ads, and only gets paid by the advertiser when purchases happen — the cost of imprecise predictions becomes noticeable almost in real-time. DSP usually charges some fraction of revenue flowing through it. E Demand-Side: The Advertiser can also be called , as the goal of this business is to sell to users as many products as possible by spending reasonable money on advertising. In our case, Advertiser is a shoe brand. The brand knows how to manufacture shoes and knows that each pair of shoes sold will bring it $20 dollars. So, Advertiser hires an ad agency and tells them to bring as many purchases as they can, as long as Advertiser is paying them less than $20 per purchase. The Advertiser The Seller Advertiser is paying Publisher for ads being shown, with SSPs and DSPs getting their cut. Advertiser in turn gets paid by the user when the user makes a purchase in Advertiser’s shop. Typical Examples and Other Business Models This is an oversimplified view of the landscape, and in reality, there is a larger variety of roles. There are countless intermediaries at each step of the exchange. For example, ad networks aggregating and representing multiple publishers, or larger DSPs aggregating demand from smaller DSPs. There are companies routing requests between multiple SSPs and DSPs, sometimes enriching them with additional information, optimizing bid prices or detecting and removing anomaly traffic, like BidSwitch. There are also companies monetizing data about users called . Typical examples of advertisers can be Adidas, Mercedes Benz or Uber. Instances of DSP are Amazon Advertising Platform or Criteo. SSP representatives are PubMatic or OpenX. And typical publishers can be Washington Post, MSN and WordPress. One of the biggest DMPs is Oracle’s BlueKai. Moreover, in reality, business roles frequently fuse together. For example, social networks like Twitter (newly X) and Facebook play the roles of publisher, SSP and DSP at the same time. Some SSPs act as DSPs by buying traffic from other SSPs, and some publishers act as DSPs by accepting advertising budgets from advertisers directly. Businesses in programmatic advertising tend to grow and consolidate models over time. For example, Facebook also owns Facebook Ad Network, and Google, while initially being a publisher and an advertiser itself, over time acquired an SSP (Google Ads Manager, previously known as DoubleClick for Publishers or DFP, and AdX, previously known as DoubleClick Ad Exchange) and a DSP (DV360, previously known as DoubleClick Bid Manager, or DBM) products. Criteo, one of the biggest DSPs, in turn, recently announced an SSP called Commerce Grid. DMPs (Data Management Platforms) Further Discussion By design, due to the ad auction and ad personalization, RTB is the most efficient possible way of ad delivery since it shows ads that maximize the expected revenue flow in the ecosystem. The user always gets the best possible ads matching their interests, the ad gets shown to the most interested users — so that the amount of expected purchases is the highest (weighted by how much money advertisers are willing to pay for each purchase generated by ads). The expected amount of money to be paid by Advertisers is maximized, and therefore Publisher's revenue is maximized too. winner ad = argmax bid_i = argmax E advertiser_payout_i clearing price = max bid_i = max E advertiser_payout_i RTB contrasts sharply with traditional advertising platforms like TV or billboards. While the latter methods might target broad demographics — placing billboards in specific neighborhoods or airing commercials on channels like the Discovery Channel to appeal to a certain interest group — they lack the capacity for nuanced personalization. Consequently, a significant portion of the audience is exposed to ads that hold little to no relevance for them. Consider the scenario where a TV commercial for female cosmetics is viewed by both women and men. In this case, the advertiser effectively reaches only female viewers, missing the mark with the male audience. This mismatch can discourage advertisers catering to the male demographic from investing in that particular TV channel. Over time, this could result in reduced advertising expenditure on such platforms, leading to a dip in revenues for these TV channels. However, the main existential challenge of programmatic advertising is that Publisher gets paid for every ad being shown, and the payments are usually based on expected purchases, but Advertiser only pays for rare purchase events that actually happened. Someone has to play the role of the buffer between predictions and reality, and in our oversimplified setup, this role is played by the DSP. It is one of the most important roles, since on the one hand, DSP is risking its own money by paying publishers for all ads and getting paid by the advertiser on conversions. It has to make sure that its predictions are correct, made within a strict time limit of hundreds of milliseconds, and that the cost of CPU is lower than DSP’s revenue from showing the ad, which makes the DSP’s task arguably the most challenging (we will discuss in the following articles how DSPs solve this challenge). On the other hand, improvements in the DSPs prediction engine are the most important growth driver for the whole ecosystem, as they lead to more effective ad allocation and generate more purchases and higher advertiser payouts. To Be Continued… In wrapping up this introductory article on programmatic advertising, we have taken a foundational look at the major players in this realm and touched upon various business models. As we delve deeper into subsequent articles, we'll closely examine each business, seeking to grasp the intricate challenges they face, both technically and mathematically. The realm of online advertising often pivots around measurable goals of revenue or cost. With feedback being tracked in real time, the implications of errors can be swiftly and accurately determined. This immediate and precise feedback, in turn, paves the way for the application of Machine Learning solutions.

Programmatic Advertising Ecosystem: The Main Players

This is the fifth piece in a series on developing XR applications and experiences using Oracle and focuses on XR applications of healthcare, vision AI, training and collaboration, and messaging, including other topics such as multi-platform development, etc. Find the links to the first four articles below: Develop XR With Oracle, Ep 1: Spatial, AI/ML, Kubernetes, and OpenTelemetry Develop XR With Oracle, Ep 2: Property Graphs and Data Visualization Develop XR With Oracle, Ep 3: Computer Vision AI, and ML Develop XR With Oracle, Ep 4: Digital Twins and Observability As with the previous posts, I will specifically show applications developed with Oracle database and cloud technologies using HoloLens 2, Oculus, iPhone, and PC and written using the Unity platform and OpenXR (for multi-platform support), Apple Swift, and WebXR. Throughout the blog, I will reference the corresponding demo video below. https://www.youtube.com/watch?v=QTe2trT2ZHM&embedable=true Extended Reality (XR) and Healthcare I will refer the reader to the first article in this series (again, the link is above) for an overview of XR, and I will not go in-depth into the vast array of technology involved in the healthcare sector but will instead focus on the XR-enablement of these topics and the use of Oracle tech to this end particularly as Oracle has an increased focus in this area via the Cerner acquisition and other endeavors. It is well known that telehealth has grown tremendously since the pandemic, peaking at 78 times the rate just a month before the pandemic and even now still leveling off at 38 times that rate. These and other numbers and their impact are well documented in numerous publications, such as this and the , which will be referenced in this article. One only needs to have heard of the to understand the extent to which XR will aid in this industry, and so again, I will not delve too deeply into attempting to prove this, but proceed to give some examples and ideas involving Oracle database and cloud technologies. McKinsey report VR/AR Associations Healthcare Forum use of XR for live surgeries by Johns Hopkins back in 2021 Vision AI and Contextual Intelligence: Real-time analysis and diagnosis The first example involves the use of the Hololens mixed-reality headset (though other headsets/devices could be used) and Oracle’s Vision AI service. It should not be much of a stretch of the imagination to envision healthcare workers wearing XR devices that provide additional functionality and information to them (and indeed, many already are). Today doctors, dentists, etc. constantly cross-reference a computer screen with a patient to check for information and orient what they see (being x-ray, MRI, etc.) and often where head-mounted lights, monocular magnifying glasses, etc. The XR solutions being discussed here are mere evolutions of that. More details on this application can be found in the research blog , but the basic details of how it works are described here. here The application running on the Hololens takes pictures with its built-in camera using the wearer’s field of vision as a reference and does so at regular intervals. This provides a hands-free experience that is, at the very least, more convenient. Still, in many situations, like in the operating room, it’s necessary for users’ hands to interact with the real (or XR) world, thus making it a more optimal solution. This also means the system can pick up contextual information that the practitioner may not be aware of or have access to while quickly gathering and processing such information without the practitioner having to explicitly instruct it, thus saving time as well. This leads to the optimization that mixed reality provides, an exciting byproduct of immersion. via secure REST calls, where they can be conveniently accessed by directly and also stored and/or accessed in the database. The Hololens then sends these pictures to OCI Object Storage the OCI Vision AI Service There are several different approaches and architectures that can be used from this point to conduct the logic and calls to the Oracle Vision AI APIs for processing the images sent by the XR device. For example, as far as language used, initial versions were written using the OCI CLI, Java, and Python, with a final Java GraalVM-native image version being used. Also, as the Java GraalM native image starts almost instantaneously when the service conducts a particular short-lived routine, it is a good candidate for a serverless function. Optionally, the OCI Notification Service can listen to Object Store changes and call serverless functions as well. The Java service receives a notification of image upload to object storage and conducts a series of actions as follows: Conducts an OCI Vision AI service API call backed by an x-ray object detection model and provides the location of the image sent by the Hololens. Receives a reply from the object detection model with the percentage chance of the x-ray being in the image and the bounding coordinates of it. Crops the original image using the bounding coordinates. Conducts another OCI Vision AI service API call backed by an x-ray classification model and provides it the cropped image of the x-ray. Receives a reply from the image classification model with percentage change of x-ray containing signs of abnormalities/pneumonia. The Hololens application receives this reply and notifies the wearer with an audible notification – this is configurable and can also be visual. In the case of this application, the information includes the picture of the cropped x-ray with its discovered details listed and stored in a virtual menu located on the wrist and viewable by the wearer only. This approach prevents the interruption of the wearer though it is also possible to overlay the results on the real-life x-ray from which they were derived. Vision AI and XR are a natural match for solutions in a number of areas, including both healthcare workers, as shown here, and people with conditions such as Parkinson’s, autism, Alzheimer’s, vision and hearing impairment, etc.. (e.g., applications/solutions that can be used in this space is described in ) this blog Motion and Sentiment Study: Real-time face and body tracking analytics Today, 95% of healthcare facilities provide remote treatment and rehabilitation. XR technologies can be used to help patients better understand their conditions and treatment options. This can help patients feel more informed and empowered in their healthcare decisions. The next example was implemented using the iPhone and Swift using Apple’s for body tracking movements. The joint coordinates of a model movement are recorded and sent to the Oracle database via Rest calls to ORDS. A person (whether it be a patient, athlete, etc.) using the application attempts to conduct the same movement, and their joint coordinates are compared to those of the model movement stored in the database. If the allowable deviation/delta of the movements is exceeded, the corresponding joints and bones are shown in red rather than green. RealityKit This feedback is given in real-time to measure progress (e.g., after a rotator cuff or other surgery) and/or to allow the user to modify their movement to match the control model as well as work on balance and coordination. These movements can, in turn, be analyzed (more on XR and Oracle Analytics in an upcoming blog), replayed, manipulated to carry out simulations, etc. These use cases, of course, extend into the sports, entertainment, etc. sectors as well. Digital Twins + Doubles and Multiplayer/Participant Messaging: Training and Collaboration A recent study reported by Unity shows that approximately 94% of companies using real-time 3D technology find it valuable for staff training and are using it to create interactive guided learning experiences. This applies across industries, including manufacturing, transportation, healthcare, retail, and many more, and some trends in the area (many of which coincide with what is shown in this blog) are discussed . here XR technologies can be used to create realistic simulations of medical scenarios, allowing healthcare professionals to practice procedures and techniques in a safe and controlled environment. For example, medical students and surgeons can use XR simulations to practice surgeries with tactile sensations giving the feedback of real surgery, and nurses can use AR simulations to practice administering medications. This can help healthcare professionals gain valuable experience without risking patient safety. In addition to teaching, XR provides a unique ability for collaboration between individuals in different locations and specialties, creating a shared virtual space where multiple users can interact with each other and with virtual objects. There are several different techniques and software options available for creating XR multiplayer/participant training and collaboration, including XR conferencing software and metaverses, Photon and others like it, Rest, WebSockets, and different types of messaging. XR conferencing software and platforms, such as Alakazam, are becoming increasingly popular and allow multiple users to participate in virtual meetings, events, training sessions, etc. Photon is a platform that allows developers to create multiplayer games and applications using Unity, Unreal Engine, and other game engines and can easily be installed on and take advantage of Oracle Cloud compute (including ). It is perhaps the most famous of such platforms, but there are others. NVIDIA GPU WebSockets are a protocol for real-time communication between web clients and servers and can be a faster and more efficient method for such use cases than Rest though Rest calls are a simple, viable option in many cases as well, and more APIs are available via Rest in general than any of the other methods. Messaging is another technique that can be used to create XR training and collaboration experiences. Systems such as Kafka and JMS have both pub/sub (multi-consumer topics) and producer/consumer (single-consumer topics) and are very flexible for different training and collaboration needs. In the training application shown in the video, I used a number of the techniques above but focused on using Oracle’s TxEventQ messaging engine (formerly known as AQ). This is a very powerful and unique offering for a number of reasons, one key one being its ability to conduct database work and messaging work in the same local transaction. This is unique and and also exactly-once message delivery so that there is no message loss and no need for the developer to write deduplication logic. perfectly suited for microservices as it provides a transaction outbox pattern This may not be necessary for conventional gaming or movie streaming but is a must for mission-critical systems and provides some extremely interesting and unique aspects for XR where the ability to reliably store a shared (3D) object (especially those that are dynamic or created via generative AI, e.g.) and the interactions/messages that have been made upon it by various participants is a very powerful tool. That is precisely what is done in the app shown in the video. The collaborative session is not only recorded in 3d for viewing as a video but the actual objects and their interactions by participants are recorded in the database and available for playback so that they can be intercepted and manipulated later on. This allows for the ability to do deeper learning and run further models (AI or otherwise), simulations, scenarios, etc., by tweaking the objects and interactions like/with playbooks. With the development of more advanced technology and more powerful devices, XR training will become more prevalent in the near future, making the training experience more immersive and interactive. One other small note is that the app in the video has healthcare workers in full 3d volumetric video captures made professionally in a studio but also one that is a simple 2d video with the green screen alpha-channel removed via a Unity shader providing a similar holographic effect with minimal effort (the quality could be better with more effort re lighting, etc.). A Zoom virtual greenscreen could be used to this end as well as free assets with animation done in Blender. Unity, Zoom, and Blender all being free and use the Oracle always-free Autonomous database, making creating the solution very accessible as far as software and cloud costs. Data EcoSystem: Telehealth and Virtual Health Centers A virtual healthcare center or hospital is a healthcare facility that provides medical services through digital channels such as video conferencing, online chat, remote monitoring, and, increasingly, XR technologies. These digital tools allow patients to access healthcare services from the comfort of their homes or remote locations without the need to physically visit a hospital or clinic. The concept of a virtual healthcare center is becoming increasingly popular due to the growing demand for telemedicine services, especially in the wake of the COVID-19 pandemic. A virtual healthcare center can provide a wide range of medical services such as primary care, specialty consultations, diagnostic tests, and prescription refills and offers convenience and flexibility to patients who can access medical services from anywhere and at any time. This can be especially beneficial for patients with mobility issues, those living in rural areas, or patients who need to consult with a specialist who is not available in their local area. It can also reduce healthcare costs for both patients and healthcare providers. By using remote consultations and monitoring, healthcare providers can reduce the need for expensive in-person consultations and hospital stays. Patients can also save money on transportation costs and time off from work. A virtual healthcare center can improve the quality of care for patients. With remote monitoring, patients can receive personalized and continuous care, with healthcare providers able to monitor their health in real-time and intervene quickly if necessary. This can result in better health outcomes and reduced hospital readmissions. However, there are also some challenges to implementing virtual healthcare centers. These include issues around data privacy and security, as well as the need for adequate internet access and digital literacy among patients. In addition, some patients may still prefer traditional in-person care, and, though decreasing in number, there are limitations to what medical services can be provided remotely. The concept of a virtual healthcare center has the potential to revolutionize the way healthcare is delivered, with benefits for both patients and healthcare providers. However, careful consideration and planning are needed to ensure that the implementation of virtual healthcare centers is safe, effective, and equitable for all patients. Oracle, with its acquisition of Cerner and increased focus on the future of healthcare, is in a one-of-a-kind position to facilitate such XR solutions by its ability to “Deliver better health insights and human-centric experiences for patients, providers, payers, and the public. Oracle Health offers the most secure and reliable healthcare solutions, which connect clinical, operational, and financial data to improve care and advance decision-making around health and well-being.” ( ). Larry Ellison made this clear at his Oracle OpenWorld presentation, where healthcare was the priority for Oracle’s future, and earlier in the year when he , “Together, Cerner and Oracle have all the technology required to build a revolutionary new health management information system in the cloud.” Oracle Health page stated Mental Health Over 20% of American adults experience mental illness, while over 2.5M youth struggle with severe depression and 800,000 people commit suicide each year in the world due to mental illness. Simultaneously, there is a growing deficit of mental health professionals, which is a nationwide issue but is particularly severe for minors and adolescents. According to the U.S. Department of Health and Human Services, the country is expected to have a shortage of 10,000 mental health professionals by 2025. Mental health is one of the areas where XR has been most widely researched and proven extremely effective as a way to deliver therapies and treatments for mental health conditions such as anxiety, depression, and PTSD. For example, VR exposure therapy can be used to help patients confront and overcome their fears by exposing them to simulations of the things they are afraid of in a controlled and safe environment. Cognitive Behavioral Therapy (CBT), XR can be used to create immersive, meditative experiences to promote mindfulness, relaxation, and stress reduction. For example, VR environments can simulate peaceful and calming natural environments, such as beaches, forests, mountains, or space. Children, the elderly, and others that may spend prolonged periods in hospitals, etc., are using XR to explore the world and socialize. Using XR and teletherapy, therapists can create virtual environments that can simulate in-person therapy sessions, providing a more immersive and personalized experience while reducing the inhibitions of the patient and even allowing the use of AR lenses and avatars for privacy. NLP (Natural Language Processing) sentiment analysis, such as that provided by the corresponding Oracle AI service, can interpret emotions and intent from verbal communication, and with advances in facial recognition and facial sentiment analysis, it is now possible with great accuracy to detect the emotions a person is feeling and/or expressing non-verbally (the same follows for body/gesture tracking). Neuro-technology and neural interfaces in coordination with XR can now interpret and give insight into human intent and measure emotion, providing even greater insight into intent and emotion. And, of course, the world is quite familiar with the greater and simpler accessibility of AI interfaces such as ChatGPT, etc., to facilitate this. As with the X-ray example given earlier, these can, in some situations, provide greater and more consistent accuracy and speed than a human but can also act as assisting technologies for healthcare workers. More examples of this will be in upcoming blogs. Additional Thoughts I have given some ideas and examples of how healthcare and XR can be used together and facilitated by Oracle. I look forward to putting out more blogs on this topic and other areas of XR with Oracle Cloud and Database soon. Please see for more information on XR and Oracle cloud and database, as well as various topics around microservices, observability, transaction processing, etc., as well as this . Also, please feel free to contact me with any questions or suggestions for new blogs and videos, as I am very open to suggestions. 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