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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. Thanks for reading and watching. my other publications recent blog about what AR is

Develop XR with Oracle, Ep 5 Healthcare, Vision AI, Training/Collaboration, and Messaging

Oracle, Ep 5 Healthcare, Vision AI, প্রশিক্ষণ/সহযোগিতা, এবং মেসেজিং সহ XR বিকাশ করুন

Entwickeln Sie XR mit Oracle, Ep 5 Healthcare, Vision AI, Training/Zusammenarbeit und Messaging

Desarrolle XR con Oracle, Ep 5 Healthcare, Vision AI, capacitación/colaboración y mensajería

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Oracle, Ep 5 Healthcare, Vision AI, 교육/협업 및 메시징을 사용하여 XR 개발

Desenvolva XR com Oracle, Ep 5 Healthcare, Vision AI, Treinamento/Colaboração e Mensagens

Oracle, Ep 5 Healthcare, Vision AI, Eğitim/İşbirliği ve Mesajlaşma ile XR'yi geliştirin

Phát triển XR với Oracle, Ep 5 Chăm sóc sức khỏe, Vision AI, Đào tạo/Cộng tác và Nhắn tin

使用 Oracle、第 5 集医疗保健、Vision AI、培训/协作和消息传递开发 XR

Inside Musk's Social Circle: Yes-men, Media Personalities, and (a lot of) right-wing VCs

Dentro del círculo social de Musk: sí-hombres, personalidades de los medios y (muchos) capitalistas de riesgo de derecha

À l'intérieur du cercle social de Musk : les hommes du oui, les personnalités des médias et (beaucoup) les VC de droite

मस्क के सोशल सर्कल के अंदर: हाँ-पुरुष, मीडिया हस्तियां, और (बहुत सारे) दक्षिणपंथी वीसी

Musk のソーシャル サークルの内部: イエスメン、メディアパーソナリティ、および (多くの) 右翼 VC

Dentro do Círculo Social de Musk: sim-men, personalidades da mídia e (muitos) VCs de direita

Bên trong vòng kết nối xã hội của Musk: Yes-men, Media Personalies, và (rất nhiều) nhà đầu tư mạo hiểm cánh hữu

马斯克的社交圈内幕：应声虫、媒体名人和（很多）右翼风投

Microsoft Excel and text-based (comma-separated values) are common file formats for data interchange, and applications can significantly benefit from implementing support for reading and writing these file formats. In this example, we will download the latest monthly market data in format and then import that data into a (which can work cross-platform) that generates a new spreadsheet containing the data in a table with a chart and some trendlines to analyze moving averages. The web application will then return the spreadsheet, which can be opened in any spreadsheet application that reads standard ( ) spreadsheet files. XLSX CSV Bitcoin-US Dollar CSV CSV C# Web Service Application XLSX XLSX Open Object XML OOXML We will use the web service to get the data (follow the link to get your free API key to use in the code examples) and , which has built-in support for importing data, generating a new spreadsheet with tables, charts, etc., and exporting a new using the following steps: AlphaVantage GrapeCity Documents for Excel .NET v5.2 CSV XLSX (using Visual Studio 2022 to create a new project). Create the Project ASP.NET Core Web API (using the web service to get monthly data in format) Query the Data AlphaVantage BTC-USD CSV . (using GrapeCity Documents for Excel.NET API) Load the CSV (rearrange columns, create a table, and create a chart with trendlines). Process the CSV (using GrapeCity Documents for Excel.NET API) Return the XLSX Create the Project Figure 1 Create New ASP.NET Core Web API Project in Visual Studio 2022 Using , create a new project ( ) and select , , and in the drop-downs to quickly find the project type , then select it and click . Visual Studio 2022 CTRL+SHIFT+N C# All Platforms WebAPI ASP.NET Core Web API Next Figure 2 Configure New ASP.NET Core Web API Project in Visual Studio 2022 Type for and select a for the project, then click . BTC_Chart_WebService Project Name Location Next Figure 3 Configure Framework Target Select for the , then click . .NET 6.0 (Long-term-support) Framework Next This will create a template project, which contains sample code to return a weather forecast. We won't need that in our project, but we can reuse and repurpose the Controller. Use ( ) to rename the Controller file in the project (under ) to : ASP.NET Core WebAPI Solution Explorer CTRL+ALT+L Controllers BTCChartController.cs Figure 4 Under , rename file to Controllers WeatherForecastController.cs BTCChartController.cs When you change the filename, will prompt you and ask if you also want to change all code references in the project – click in the dialog: Visual Studio Yes Figure 5 Rename code references - click here Yes Then select the project file and delete it from the project ( ), then click in the dialog asking you to confirm: WeatherForecast.cs DEL Yes Figure 6 Confirm deleting WeatherForecast.cs from the project Now in ( ), right-click and select : Solution Explorer CTRL+ALT+L Dependencies Manage NuGet Packages Figure 7 Open from Manage NuGet Packages Solution Explorer Dependencies Search for on and install version (or later) in the project. GrapeCity.Documents.Excel nuget.org 5.2.0 Query the Data Now in ( ), select the file for editing, and comment out the unneeded code for related to the weather forecast: Solution Explorer CTRL+ALT+L BTCChartController.cs Summaries BTCChartController public class BTCChartController : ControllerBase
{
    //    private static readonly string[] Summaries = new[]
    //    {
    //    "Freezing", "Bracing", "Chilly", "Cool", "Mild", "Warm", "Balmy", "Hot", "Sweltering", "Scorching"
    //}; Now add the following code below the commented code above to query the web service for the monthly data in format (substituting in the code): AlphaVantage BTC-USD CSV your API key GetCSVData //    private static readonly string[] Summaries = new[]
//    {
//    "Freezing", "Bracing", "Chilly", "Cool", "Mild", "Warm", "Balmy", "Hot", "Sweltering", "Scorching"
//};
// Get the CSV data from the AlphaVantage web service
private string GetCsvData()
{
    string csv;
    string API_KEY = "YOUR_KEY_HERE";
    string QUERY_URL = $"https://www.alphavantage.co/query?function=DIGITAL_CURRENCY_MONTHLY&amp;symbol=BTC&amp;market=USD&amp;apikey={API_KEY}&amp;datatype=csv";
    Uri queryUri = new Uri(QUERY_URL);
    using (HttpClient client = new HttpClient())
    {
        Task<string> t = client.GetStringAsync(queryUri);
        while (!t.IsCompleted)
            t.Wait();
        csv = t.Result;
    }
    return csv;
} Load the CSV Now copy the following code into the method, updating the in the attribute to , and also change the return type to , then comment out the code related to the weather forecast before adding the code to call and import it into the new : Get() Name HttpGet GetBTC-USDChartWorkbook FileContentResult GetCsvData() Workbook BTCChartController.Get [HttpGet(Name = "GetBTC-USDChartWorkbook")]
//public IEnumerable<WeatherForecast> Get()
public FileContentResult Get()
{
    //return Enumerable.Range(1, 5).Select(index => new WeatherForecast
    //{
    //    Date = DateTime.Now.AddDays(index),
    //    TemperatureC = Random.Shared.Next(-20, 55),
    //    Summary = Summaries[Random.Shared.Next(Summaries.Length)]
    //})
    //.ToArray();
 
    // first get CSV data
    string csv = GetCsvData();
 
    // create new workbook
    IWorkbook wbk = new Workbook();
    // open CSV data in GcExcel using MemoryStream
    using (Stream s = new MemoryStream())
    {   // convert to byte array using UTF8 encoding
        byte[] arr = System.Text.Encoding.UTF8.GetBytes(csv.ToCharArray());
        s.Write(arr);
        s.Seek(0, SeekOrigin.Begin);
        // open CSV in workbook
        wbk.Open(s, OpenFileFormat.Csv);
    } Process the CSV Now copy the following code to process the in the workbook: CSV BTCChartController.Get (cont) // get the worksheet with the CSV data
IWorksheet wks = wbk.Worksheets[0];
// move volume to column B, before open/high/low/close
// it works better to create the StockVOHLC chart with
// the series in the correct order (date-volume-open-high-low-close)
wks.Range["B:B"].Insert();
wks.Range["K:K"].Copy(wks.Range["B:B"]);
wks.Range["K:K"].Delete();
// get the range containing the CSV data
IRange used = wks.UsedRange;
// create a new Table for the CSV data named "BTC_Monthly"
ITable tbl = wks.Tables.Add(used, true);
tbl.Name = "BTC_Monthly";
// auto-fit the columns in the table to show all the cell values
used.AutoFit();
// add the StockVOHLC chart shape over the table (the table is completely beneath the chart)
IShape shape = wks.Shapes.AddChart(ChartType.StockVOHLC, 0, 0, used.Width, used.Height);
// get the IChart from the shape
IChart chart = shape.Chart;
// set the chart title
chart.ChartTitle.Text = "BitCoin Monthly Open-High-Low-Close-Volume";
// add the chart series (the first 6 columns in the table)
chart.SeriesCollection.Add(wks.Range[0, 0, used.RowCount, 6], RowCol.Columns, true, true);
// add trend line for Volume series in Blue
ITrendline voltrend = chart.SeriesCollection[0].Trendlines.Add();
voltrend.Name = "3 Month Moving Avg VOL";
voltrend.Type = TrendlineType.MovingAvg;
voltrend.Period = 3;
voltrend.Format.Line.Color.RGB = Color.Blue;
voltrend.Format.Line.DashStyle = LineDashStyle.RoundDot;
// add trend line for High series in Green
ITrendline hightrend = chart.SeriesCollection[2].Trendlines.Add();
hightrend.Name = "3 Month Moving Avg HIGH";
hightrend.Type = TrendlineType.MovingAvg;
hightrend.Period = 3;
hightrend.Format.Line.Color.RGB = Color.Green;
hightrend.Format.Line.DashStyle = LineDashStyle.RoundDot;
// add trend line for Low series in Red
ITrendline lowtrend = chart.SeriesCollection[3].Trendlines.Add();
lowtrend.Name = "3 Month Moving Avg LOW";
lowtrend.Type = TrendlineType.MovingAvg;
lowtrend.Period = 3;
lowtrend.Format.Line.Color.RGB = Color.Red;
lowtrend.Format.Line.DashStyle = LineDashStyle.RoundDot; First, the code gets the with the data and rearranges the columns to put the column between the and columns. Then it creates a named containing the data and auto-fits the columns in the . Then the code adds a new in the worksheet of type ( ) over the entire table range, sets the chart title, adds the series to the chart, and then creates three . The trendlines show three-month moving averages for , , and . IWorksheet CSV Volume Date Open Table BTC_Monthly CSV Table Chart StockVOHLC Volume-Open-High-Low-Close Trendlines Volume in blue High in green Low in red Return the XLSX Now copy the following code to save the to and return it from the web service: Workbook XLSX // Save Workbook to XLSX and return from web service as "BTC_Chart.xlsx"
        using (MemoryStream ms = new MemoryStream())
        {
            wbk.Save(ms, SaveFileFormat.Xlsx);
            ms.Seek(0, SeekOrigin.Begin);
            byte[] bytes = ms.ToArray();
            return File(bytes, "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "BTC_Chart.xlsx");
        }
    } // Get()
} // BTCChartController Now the project is complete and ready to run! Press F5 to run in debug, and you can try it out: Figure 8 Press to Run in Debug F5 BTC_Chart_WebService Open the dropdown and click Try it out: Figure 9 Press to test the web service Try it out Then click Execute, and you should soon see the success result and the link to download the XLSX file: Figure 10 Success result and file download link XLSX The resulting chart looks like this in Excel: Download the for this blog. sample Ready to Try it Out? Download GrapeCity Documents for Excel Today! Also Here

Importing, Exporting CSV and Excel XLSX in .NET C# Applications

This story was originally published by . ProPublica’s Craig Silverman The internet giant may have provided Sberbank-owned RuTarget with unique mobile phone IDs, IP addresses, location information and details about users’ interests and online activity. The day after Russia’s February invasion of Ukraine, Senate Intelligence Committee Chairman sent a to Google warning it to be on alert for “exploitation of your platform by Russia and Russian-linked entities,” and calling on the company to audit its advertising business’s compliance with economic sanctions. Mark Warner letter But as recently as June 23, Google was sharing potentially sensitive user data with a sanctioned Russian ad tech company owned by Russia’s largest state bank, according to a new report provided to ProPublica. Google allowed RuTarget, a Russian company that helps brands and agencies buy digital ads, to access and store data about people browsing websites and apps in Ukraine and other parts of the world, . Adalytics identified close to 700 examples of RuTarget receiving user data from Google after the company was added to a U.S. Treasury list of sanctioned entities on Feb. 24. The data sharing between Google and RuTarget stopped four months later on June 23, the day ProPublica contacted Google about the activity. according to research from digital ad analysis firm Adalytics RuTarget, which also operates under the name Segmento, is owned by Sberbank, a Russian state bank that the Treasury described as “uniquely important” to the country’s economy when it hit the lender with initial sanctions. RuTarget was later listed in an that imposed full blocking sanctions on Sberbank and other Russian entities and people. The sanctions mean U.S. individuals and entities are not supposed to conduct business with RuTarget or Sberbank. April 6 Treasury announcement Of particular concern, the analysis showed that Google shared data with RuTarget about users browsing websites based in Ukraine. This means Google may have turned over such critical information as unique mobile phone IDs, IP addresses, location information and details about users’ interests and online activity, data that U.S. senators and experts say could be used by Russian military and intelligence services to track people or zero in on locations of interest. Last April, a bipartisan group of U.S. senators sent to Google and other major ad technology companies warning of the national security implications of data shared as part of the digital ad buying process. They said this user data “would be a goldmine for foreign intelligence services that could exploit it to inform and supercharge hacking, blackmail, and influence campaigns.” a letter Google spokesperson Michael Aciman said that the company blocked RuTarget from using its ad products in March, and that RuTarget has not purchased ads directly via Google since then. He acknowledged the Russian company was still receiving user and ad buying data from Google before being alerted by ProPublica and Adalytics. “Google is committed to complying with all applicable sanctions and trade compliance laws,” Aciman said. “We’ve reviewed the entities in question and have taken appropriate enforcement action beyond the measures we took earlier this year to block them from directly using Google advertising products.” Aciman said this action includes not only preventing RuTarget from further accessing user data, but from purchasing ads through third parties in Russia that may not be sanctioned. He declined to say whether RuTarget had purchased ads via Google systems using such third parties, and he did not comment on whether data about Ukrainians had been shared with RuTarget. Krzysztof Franaszek, who runs Adalytics and authored the report, said RuTarget’s ability to access and store user data from Google could open the door to serious potential abuse. “For all we know they are taking that data and combining it with 20 other data sources they got from God knows where,” he said. “If RuTarget’s other data partners included the Russian government or intelligence or cybercriminals, there is a huge danger.” In a statement to ProPublica, Warner, a Virginia Democrat, called Google’s failure to sever its relationship with RuTarget alarming. “All companies have a responsibility to ensure that they are not helping to fund or even inadvertently support Vladimir Putin’s invasion of Ukraine. Hearing that an American company may be sharing user data with a Russian company — owned by a sanctioned, state-owned bank no less — is incredibly alarming and frankly disappointing,” he said. “I urge all companies to examine their business operations from top to bottom to ensure that they are not supporting Putin’s war in any way.” Google’s initial failure to fully enforce sanctions on RuTarget highlights how money and data can flow through its market-leading digital advertising systems with little oversight or accountability. showed that Google had continued serving ads on Russian websites that had been on the Treasury sanctions list for years. In June, that Google helped place, and earned money from, more than 100 million gun ads, despite the company’s strong public stance against accepting such ads. An April report from Adalytics ProPublica reported The findings about RuTarget also come as Google and other tech companies face intense scrutiny from legislators about their handling of personal data. , who sits on the Senate Intelligence Committee, criticized Google for its failure last year to provide him and his colleagues with a list of the foreign-owned companies it shares ad data with. Sen. Ron Wyden, D-Ore. “Google has refused to disclose [to senators] whether its ad network makes Americans’ data available to foreign companies in Russia, China and other high-risk countries,” he said in a statement to ProPublica. “It is time for Congress to act and pass my bipartisan bill, the Protecting Americans’ Data From Foreign Surveillance Act, which would force Google and other networks to radically change how they do business and ensure unfriendly foreign governments don’t have unfettered access to Americans’ sensitive information.” Wyden and his colleagues introduced the bipartisan bill last week to prevent sensitive data about Americans from being sold or transferred to “high-risk foreign countries.” Wyden and a different group of Senate colleagues also sent a letter to Federal Trade Commission Chair Lina Khan last week asking her to investigate Google and Apple for enabling mobile advertising IDs in cellphones. These unique IDs can be combined with other data to personally identify users. Wyden’s letter cited mobile IDs as one way that Google and Apple transformed “online advertising into an intense system of surveillance that incentivizes and facilitates the unrestrained collection and constant sale of Americans’ personal data.” Aciman of Google said that the mobile advertising ID was created to give users control and privacy, and that Google does not allow the sale of user data. “The advertising ID was created to give users more control and provide developers with a more private way to effectively monetize their app,” he said. “Additionally, Google Play has in place that prohibit using this data for purposes other than advertising and user analytics. Any claims that advertising ID was created to facilitate data sales are simply false.” policies Bidstream Data Under Scrutiny At the heart of both the senators’ concerns and the Adalytics report is the data collected on global internet users that gets passed between companies as part of the digital ad buying process. This treasure trove of information can include a person’s unique mobile ID, IP address, location information and browsing habits. When passed between companies to facilitate ad buying, the trove is called bidstream data. And it’s essential to the roughly digital ad industry that is dominated by Google. half a trillion dollar Many digital ads are placed as a result of a real-time auction in which the seller of ad space, such as a website, is connected with potential buyers, like brands and agencies. An auction starts when a user visits a website or app. Within milliseconds, data collected about this user is shared with potential ad buyers to help them decide whether to bid to show an ad to the user. Regardless of whether they bid or not, ad buying platforms like RuTarget receive and store this bidstream data, helping them automate the amassing of rich repositories of data over time. The auction process is run by ad exchanges. They connect buyers and sellers and facilitate the sharing of bidstream data between them in conjunction with a process called cookie syncing. Google operates the world’s largest ad exchange, and RuTarget is one of many companies it shares bidstream data with. The more RuTarget connects with ad exchanges like Google, the more information it can gather and combine with data collected from other online and offline sources. Justin Sherman, a fellow at Duke’s Sanford School of Public Policy who runs a project focused on data brokers, said bidstream data is largely unregulated and can be highly sensitive, even if it does not include personal information such as names or emails. “There’s growing attention to the ways in which our data ecosystem and our ecosystem of data brokers and advertisers gives away or sends or sells highly sensitive information on Americans to foreign entities,” he said. “There is also concern about foreign entities illicitly accessing that information.” Google Failed to Disclose Bidstream Data Partners Fears over the ill-usage of the information led Warner, Wyden and four colleagues to ask Google and six other ad exchanges in April 2021 to list the domestic and foreign partners they shared bidstream data with in the past three years. They warned that this data could have serious implications for U.S. national security. “Few Americans realize that some auction participants are siphoning off and storing ‘bidstream’ data to compile exhaustive dossiers about them. In turn, these dossiers are being openly sold to anyone with a credit card, including to hedge funds, political campaigns, and even to governments,” they wrote in letters to AT&T, Index Exchange, Google, Magnite, OpenX, PubMatic, Twitter and Verizon. Google a few weeks later but refused to list the companies it shares bidstream data with, citing “non-disclosure obligations.” responded reveals concerns about the accuracy of Google’s response. He identified eight pages on Google’s support website that list hundreds of foreign and domestic companies that are eligible to receive bidstream data from it. One list contains over 300 companies, of which 19 are Chinese owned or headquartered and 16 are based in Russia, including RuTarget. Franaszek’s research Franaszek also found that some of these companies publicly disclosed their relationship with Google. And, , some of Google’s competitors disclosed to the senators the foreign partners they share data with. as reported by Vice This raises questions as to what Google was referring to when it said nondisclosure obligations prevent it from naming its partners, according to Franaszek. “Google was publicizing, on its own website, lists of foreign [partners] months before they told the senators that,” he said. Google’s Aciman said the lists on Google’s website do not disclose the nature of its relationship with the companies, and he reiterated that it has nondisclosure obligations with companies who act as bidders. One of the on Google’s site (“Ad Manager Certified External Vendors”) includes a column that describes what each Google vendor does. At least 13 of the companies are publicly identified as “RTB bidders,” meaning they act as bidders in Google’s real-time ad auction process. lists Publishers Sharing Data With RuTarget The user data shared by Google with RuTarget and other potential bidders is drawn from millions of websites and apps that rely on the Silicon Valley giant to help them earn money from ads. And many would likely be surprised to learn that a sanctioned Russian ad company was until two weeks ago able to harvest information about their visitors. Because of its relationship with Google, RuTarget is publicly listed as a recipient of user data by major publishers including Reuters and ESPN. This means RuTarget can receive data from these companies about the millions of people who visit their online properties each month. Like other publishers, ESPN and Reuters list RuTarget as a recipient of user data in cookie consent popups shown to users browsing their sites from the EU and other jurisdictions with data privacy laws requiring such disclosures. A spokesperson for Reuters said the companies shown in its consent popup, including RuTarget, come from a list of vendors provided by Google. “This list of vendors is managed by Google, and Reuters uses Google’s list of vendors on our website. We understand that Google suspended buyers and bidders based in Russia, and we have no record of any transactions with RuTarget since April 6,” Heather Carpenter of Reuters said. ESPN did not respond to a request for comment. As a Google partner, it’s possible that data about users browsing ProPublica’s website has at some point been shared with RuTarget. The opaque and technical nature of digital advertising makes it difficult to know for sure. Jason Kint, head of the digital publisher trade group Digital Content Next, said Google’s market power leaves publishers with little choice except to work with the company. “Premium publishers have to trust Google for a significant number of services that they depend on,” he said. “This is another example of misplaced trust. I’m just incredibly disappointed in Google.”

Sberbank-Owned RuTarget Harvested User Data for Months via Google 

Programmatically, editing an Excel file can be difficult for two primary reasons. Users have to maintain a consistent interface and document structure which are not always easy to do when working with spreadsheets. The other reason is that it's not always clear what code will work in certain circumstances, especially if complex calculations are involved. There are many different reasons why editing excels files programmatically is complex. Programmers have to convert the data in the Excel file into a usable format; then, they have to parse the data and parse it back, which is not as straightforward. Here I come up with a solution for this problem. I will show you how we can read and edit excel files programmatically without any hassle using different Excel libraries. Let's get started: FastExcel: .NET Excel Library FastExcel is a versatile and powerful library for opening, editing, saving Excel Files, reading, and other important Excel functionalities. It supports all .NET project templates like ASP.NET, Windows Applications, and .NET Core Applications. FastExcel is very easy to use in .NET applications for developers. FastExcel doesn't need the installation of Microsoft Office on a local machine where we have to use the FastExcel library. And FastExcel also doesn't use Excel Interop for excel operations. FastExcel makes working with excel files in the .NET environment very easy, rapid, and straightforward. FastExcel makes it easy to perform all Excel operations and calculations without detailed programming information. FastExcel enables the developers to perform many special excel operations by writing a few lines of code like the sum function, multiple rows, total columns, reading excel files, writing excel files, modifying excel tables, adding columns and rows, removing columns, and rows. Let's have a look at a few code examples: Code Example Creating Excel file using (FastExcel.FastExcel fastExcel = new FastExcel.FastExcel(new FileInfo("Template.xlsx"), new FileInfo("Output.xlsx")))
{
 //Create a worksheet with some rows
 var worksheet = new Worksheet();
 var rows = new List();
 for (int rowNumber = 1; rowNumber < 100000; rowNumber++)
 {
  List cells = new List();
  for (int columnNumber = 1; columnNumber < 13; columnNumber++)
  {
   cells.Add(new Cell(columnNumber, columnNumber * DateTime.Now.Millisecond));
  }
  cells.Add(new Cell(13,"FileFormat" + rowNumber));
  cells.Add(new Cell(14,"FileFormat Developer Guide"));

  rows.Add(new Row(rowNumber, cells));
 }
 worksheet.Rows = rows;

 fastExcel.Write(worksheet,"sheet1");
} Rows and cells management using (FastExcel.FastExcel fastExcel = new FastExcel.FastExcel(new FileInfo("input.xlsx")))
{
 //Create a some rows in a worksheet
 var worksheet = new Worksheet();
 var rows = new List();

 for (int rowNumber = 1; rowNumber < 100000; rowNumber += 50)
 {
  List cells = new List();
  for (int columnNumber = 1; columnNumber < 13; columnNumber += 2)
  {
   cells.Add(new Cell(columnNumber, rowNumber));
  }
  cells.Add(new Cell(13,"File Format Developer Guide"));

  rows.Add(new Row(rowNumber, cells));
 }
 worksheet.Rows = rows;
 // Read the data
 fastExcel.Update(worksheet,"sheet1");
} Pricing Plan It is an open-source library. So, there is no pricing plan for it. You can use it for your basic functionalities of excel. IronXL: C# Excel Library IronXL is a C# excel library that assists the user with the functionality of fast reading and writing of excel spreadsheets. It takes less memory while running in the background. It doesn't use OpenXML SDK for editing or interacting with data. It does the editing of XML files by itself. IronXL only requires .NET Framework 4.5 or higher and .NET Core 2.0. However, it supports the latest .NET frameworks like .NET framework 7. We can add new sheets, and cell ranges, add data, and many other things using IronXL Library. Let's have a look at code examples: Code Example Reading Excel file using IronXL;
//Supported spreadsheet formats for reading include: XLSX, XLS, CSV and TSV
WorkBook workbook = WorkBook.Load("data.xlsx");
WorkSheet sheet = workbook.WorkSheets.First();
//Select cells easily in Excel notation and return the calculated value, date, text or formula
int cellValue = sheet["A2"].IntValue;
// Read from Ranges of cells elegantly.
foreach (var cell in sheet["A2:B10"])
{
    Console.WriteLine("Cell {0} has value '{1}'", cell.AddressString, cell.Text);
} Excel Range Cell using IronXL;
using System. Linq;
 
WorkBook workbook = WorkBook.Load("test.xls");
WorkSheet sheet = workbook.WorkSheets.First();
 

var range = sheet["A2:A8"];
//This is how we can iterate over our range and read or edit any cell
foreach (var cell in range)
{
    Console.WriteLine(cell.Value);
}
 
 
// Another Example
var oneMoreRange = sheet["A9:A10"];
 
//This is how we can combine our ranges into a single selection
var resultRange = range + oneMoreRange;
 

foreach (var cell in resultRange)
{
    Console.WriteLine(cell.Value);
} IronXL supports all major operating systems like Windows, Linux, and macOS. Pricing Plan IronXL has three pricing plans. The basic package starts from $749. But it is free for development purposes. It also offers 30 days free trial. You can see more details in the given picture. EPPlus: .NET Excel Library EPPlus is a C Excel Library that provides access to Excel's objects and methods, including VBA and API. With EPPlus, developers can harness the power of Excel to build quality, complex applications or whip up a quick spreadsheet. The library provides functions for managing cells, rows, and columns; working with worksheets, creating pivot tables, charts and graphs; manipulating pivot table fields; working with images, and exporting data to other formats. EPPlus has no dependencies on any other library such as Microsoft Excel. EPPlus is not just a static library - it is constantly evolving in response to changes in the Excel spreadsheet environment and user needs. You can observe their changelog to see how it is evolving. EPPlus is distributed by NuGet. Version 5 of EPPlus supports .NET Framework from version 3.5 and supports .NET Core from version 2.0. Pricing Plan The pricing plans of EPPlus are a little bit confusing. They didn't provide complete information on how the pricing plans work and whether it is continual or if we have to buy year after year. Unfortunately, EPPlus didn't give any code example to demonstrate how it will use. And it also didn't provide any tutorial or free trial version. Summary All libraries are fantastic and the best in their fields. But every library has its pros and cons. EPPlus doesn't have any tutorial guide, and developers are unsure how this library will workout. However, it has a Github repo. And its pricing plan is not much clear. Fast Excel library is also sound, but it has limited features. But it is an open-source library, so we can use it in commercial projects. But in my opinion, a short excel library is not compatible with large projects. It would help if you went with IronXL or EPPlus library to use excel operations in your big commercial project. IronXL library is a mature library with all the advanced features we have to use in an advanced excel application. The excellent point is that it offers 30 day free trial in commercials, so it becomes easy to test if it is compatible.

3 Excel Libraries Every .NET Developer Must Know

3 bibliotecas de Excel que todo desarrollador de .NET debe conocer

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