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  Fillrate

  In computer graphics, a video card's pixel fillrate refers to the number of pixels that can be rendered on the screen and written to video memory in one second. Pixel fillrates are given in megapixels per second or in gigapixels per second (in the case of newer cards), and are obtained by multiplying the number of render output units (ROPs) by the clock frequency of the graphics processing unit (GPU) of a video card. A similar concept, texture fillrate, refers to the number of texture map elements (texels) the GPU can map to pixels in one second. Texture fillrate is obtained by multiplying the number of texture mapping units (TMUs) by the clock frequency of the GPU. Texture fillrates are given in mega or gigatexels per second. However, there is no full agreement on how to calculate and report fillrates. Another possible method is to multiply the number of pixel pipelines by the GPU's clock frequency. The results of these multiplications correspond to a theoretical number. The actual fillrate depends on many other factors. In the past, the fillrate has been used as an indicator of performance by video card manufacturers such as ATI and NVIDIA, however, the importance of the fillrate as a measurement of performance has declined as the bottleneck in graphics applications has shifted. For example, today, the number and speed of unified shader processing units has gained attention. Although fillrate doesn't provide a substantial bottleneck in games, it can still provide a bottleneck for certain parts of the game, for example applying a gaussian blur can be bottlenecked by fillrate. Scene complexity can be increased by overdrawing, which happens when an object is drawn to the frame buffer, and another object (such as a wall) is then drawn on top of it, covering it up. The time spent drawing the first object is thus wasted because it is not visible. When a sequence of scenes is extremely complex (many pixels have to be drawn for each scene), the frame rate for the sequence may drop. When designing graphics intensive applications, one can determine whether the application is fillrate-limited (or shader limited) by seeing if the frame rate increases dramatically when the application runs at a lower resolution or in a smaller window. Although this is not a full-proof method, modern videogame engines can dynamically reduce the level-of-detail required and thereby reducing fillrate-limited applications. The best way to find fillrate bottlenecks is to use GPU vendor software like NVIDIA Nsight Graphics, AMD Radeon GPU Profile and the Intel Graphics Performance Analyzers.

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  List of data science software

  This is a list of data science software and platforms used in data science, which includes programming languages, programming environments, machine learning frameworks, data engineering tools, statistical software, data analysis, plotting, MLOps systems, and more. == Programming languages == == Development environments == These interactive notebooks, IDEs, and platforms provide specialised development environments. Apache Zeppelin Architect — Eclipse (software) CoCalc Dataiku Data Science Studio FreeMat GNU Octave Google Colab DataSpell Jupyter Notebook / JupyterLab Kaggle Notebooks MATLAB O-Matrix PyCharm RStudio SAS (software) and SAS Studio Spyder Visual Studio Code == Machine and deep learning software == The Machine learning / deep learning tools support development in those fields. == Data engineering == Examples of Data engineering tools. Apache Airflow Apache Flink Apache Hadoop Apache Kafka Apache NiFi Apache Spark Dask Data build tool (dbt) == Data mining == Examples of Data mining tools. === Free and open-source === === Proprietary === == Database management == === List of RDBMS === ==== Proprietary ==== == Data warehouses == Data warehouse environments include: Amazon Redshift Snowflake Google BigQuery Microsoft Azure Synapse Teradata Vertica == Data lakes == Data lake environments include: Apache Hadoop Cloudera Databricks Delta Lake Amazon S3 Google Cloud Storage Azure Data Lake == Algorithms == Apriori algorithm – frequent itemset mining and association rule learning in market basket analysis Backpropagation – algorithm for training artificial neural networks using gradient descent Decision Trees – tree-based algorithm for classification and regression Expectation–maximization algorithm – iterative procedure for maximum likelihood estimation with latent variables Gradient descent – iterative optimization algorithm for minimizing a loss function ID3 algorithm – used to generate a decision tree from a dataset K-Means – clustering algorithm based on minimizing within-cluster distances K-Nearest Neighbors (KNN) – instance-based learning and classification method Linear regression – estimation method for predicting a dependent variable based on independent variables Logistic regression – classification algorithm for predicting a binary outcome Naive Bayes – probabilistic classifier based on Bayes' theorem Ordinary least squares – estimation method for parameters in linear regression PageRank – graph-based algorithm for link analysis and search ranking Principal component analysis – technique to reduce high-dimensional data while preserving variance Q-learning – reinforcement learning algorithm for learning optimal actions Random forest – ensemble of decision trees for improved classification or regression Sequential minimal optimization – solver for training support vector machines Stochastic gradient descent – randomized variant of gradient descent for large-scale machine learning Support Vector Machines (SVM) – algorithm for finding a hyperplane to separate classes == Statistical software == === Open-source === === Public domain === CSPro Dataplot Epi Map X-13ARIMA-SEATS === Freeware === BV4.1 MINUIT WinBUGS Winpepi === Proprietary === == Data processing == Tools for Data processing and analysis: == Data and information visualization == Software for Data visualization: == Plotting software == Software for plotting data to support processing and visualise results. == Maps and geospatial visualization == ArcGIS Carto Epi Map GeoDA Google Earth Engine Leaflet Mapbox MountainsMap QGIS == Machine learning == MLOps and model deployment: BentoML Data Version Control (DVC) Kubeflow MLflow Seldon Core Streamlit TensorFlow Serving Weights & Biases == Data repositories == Kaggle – platform for data science competitions, datasets, and notebooks. OpenML – collaborative platform for sharing datasets, algorithms, and experiments. University of California, Irvine Machine Learning Repository Zenodo – open-access repository supported by CERN and the EU. == Educational data science software == Kaggle – online platform for data science education, competitions, datasets, and collaborative learning. KNIME – open-source data analytics platform used for teaching data science, machine learning, and workflow-based analysis. RapidMiner – used in academic research and education for data mining and machine learning. Statistics Online Computational Resource (SOCR) – online tools and instructional resources for statistics education. Tanagra (machine learning) – data mining software developed for research and teaching purposes. TinkerPlots – explore and analyze data through visual modeling.

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  TAChart

  TAChart is a component for the Lazarus IDE that provides charting services. Similar to Tchart and Teechart for Delphi it supports a collection of different chart types including bar charts, pie charts, line charts and point series. Apart from a screen canvas, output is possible in form of SVG, OpenGL, printer, WMF, and other formats. TAChart is bundled with the Lazarus Component Library. Although not intended to be a TChart clone, why its usage differs in certain points, its basic functionality is very similar and some source code written for TeeChart may be reused. == History == The first version of TAChart was developed by Philippe Martinole for the TeleAuto project, a program for automation of astronomic observations. Later functionality was introduced by Luis Rodrigues while porting the Epanet application from Delphi to Lazarus. In the ensuing years the code has extensively rewritten, expanded and is now maintained by Alexander Klenin. == Data sources == TAChart is able to use input from various sources. Examples include lists of real values, user defined buffers in the computer's memory, vectors of random values, fields in databases, calculated values provided by pre-defined functions and results of embedded code written in Pascal Script

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  Cloudlet

  A cloudlet is a mobility-enhanced small-scale cloud datacenter that is located at the edge of the Internet. The main purpose of the cloudlet is supporting resource-intensive and interactive mobile applications by providing powerful computing resources to mobile devices with lower latency. It is a new architectural element that extends today's cloud computing infrastructure. It represents the middle tier of a 3-tier hierarchy: mobile device - cloudlet - cloud. A cloudlet can be viewed as a data center in a box whose goal is to bring the cloud closer. The cloudlet term was first coined by M. Satyanarayanan, Victor Bahl, Ramón Cáceres, and Nigel Davies, and a prototype implementation is developed by Carnegie Mellon University as a research project. The concept of cloudlet is also known as follow me cloud, and mobile micro-cloud. == Motivation == Many mobile services split the application into a front-end client program and a back-end server program following the traditional client-server model. The front-end mobile application offloads its functionality to the back-end servers for various reasons such as speeding up processing. With the advent of cloud computing, the back-end server is typically hosted at the cloud datacenter. Though the use of a cloud datacenter offers various benefits such as scalability and elasticity, its consolidation and centralization lead to a large separation between a mobile device and its associated datacenter. End-to-end communication then involves many network hops and results in high latencies and low bandwidth. For the reasons of latency, some emerging mobile applications require cloud offload infrastructure to be close to the mobile device to achieve low response time. In the ideal case, it is just one wireless hop away. For example, the offload infrastructure could be located in a cellular base station or it could be LAN-connected to a set of Wi-Fi base stations. The individual elements of this offload infrastructure are referred to as cloudlets. == Applications == Cloudlets aim to support mobile applications that are both resource-intensive and interactive. Augmented reality applications that use head-tracked systems require end-to-end latencies of less than 16 ms. Cloud games with remote rendering also require low latencies and high bandwidth. Wearable cognitive assistance systems combine devices such as Google Glass with cloud-based processing to guide users through complex tasks. This futuristic genre of applications is characterized as “astonishingly transformative” by the report of the 2013 NSF Workshop on Future Directions in Wireless Networking. These applications use cloud resources in the critical path of real-time user interaction. Consequently, they cannot tolerate end-to-end operation latencies of more than a few tens of milliseconds. Apple Siri and Google Now which perform compute-intensive speech recognition in the cloud, are further examples in this emerging space. == Cloudlet vs Cloud == There is significant overlap in the requirements for cloud and cloudlet. At both levels, there is the need for: (a) strong isolation between untrusted user-level computations; (b) mechanisms for authentication, access control, and metering; (c) dynamic resource allocation for user-level computations; and, (d) the ability to support a very wide range of user-level computations, with minimal restrictions on their process structure, programming languages or operating systems. At a cloud datacenter, these requirements are met today using the virtual machine (VM) abstraction. For the same reasons they are used in cloud computing today, VMs are used as an abstraction for cloudlets. Meanwhile, there are a few but important differentiators between cloud and cloudlet. === Rapid provisioning === Different from cloud data centers that are optimized for launching existing VM images in their storage tier, cloudlets need to be much more agile in their provisioning. Their association with mobile devices is highly dynamic, with considerable churn due to user mobility. A user from far away may unexpectedly show up at a cloudlet (e.g., if he just got off an international flight) and try to use it for an application such as a personalized language translator. For that user, the provisioning delay before he is able to use the application impacts usability. === VM handoff across cloudlets === If a mobile device user moves away from the cloudlet he is currently using, the interactive response will degrade as the logical network distance increases. To address this effect of user mobility, the offloaded services on the first cloudlet need to be transferred to the second cloudlet maintaining end-to-end network quality. This resembles live migration in cloud computing but differs considerably in a sense that the VM handoff happens in Wide Area Network (WAN). == OpenStack++ == Since the cloudlet model requires reconfiguration or additional deployment of hardware/software, it is important to provide a systematic way to incentivise the deployment. However, it can face a classic bootstrapping problem. Cloudlets need practical applications to incentivize cloudlet deployment. However, developers cannot heavily rely on cloudlet infrastructure until it is widely deployed. To break this deadlock and bootstrap the cloudlet deployment, researchers at Carnegie Mellon University proposed OpenStack++ that extends OpenStack to leverage its open ecosystem. OpenStack++ provides a set of cloudlet-specific APIs as OpenStack extensions. == Commercial implementations and standardization effort == By 2015 cloudlet based applications were commercially available. In 2017 the National Institute of Standards and Technology published draft standards for fog computing in which cloudlets were defined as nodes on the fog architecture.

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  Hancom Office

  Hancom Office is a proprietary office suite that includes a word processor, spreadsheet software, presentation software, and a PDF editor as well as their online versions accessible via a web browser. It is primarily addressed to Korean users. Hancom Office is written in Java and C++ that runs on Android, iOS, macOS and Windows platforms. == Products == Hangul - Hangul is a word processor developed by Hancom. It is a product that eliminates the inconvenience of the original Hangul word processor, which was limited to Hangul cards or PC models. Originally, the name was written using the '아래아' character, a vowel letter that is obsolete in modern Korean, and it was referred to as 'HWP' (an abbreviation for Hangul Word Processor), '아래아 한글' (Arae-a Hangul), '한/글' (Han/Geul), and so on. Hangul is currently the most widely used word processor in South Korea, often used alongside Microsoft Word. HanWord - word processor compatible with Word HanCell - spreadsheet program HanShow - presentation program Hancom Office Hanword Viewer - For viewing documents created by Hancom Office or Microsoft Office

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  Open Data Center Alliance

  opendatacenteralliance.org appears to have been closed down. The Open Data Center Alliance is an independent organization created in Oct. 2010 with the assistance of Intel to coordinate the development of standards for cloud computing. Approximately 100 companies, which account for more than $50bn of IT spending, have joined the Alliance, including BMW, Royal Dutch Shell and Marriott Hotels. "The Alliance's Cloud 2015 vision is aimed at creating a federated cloud where common standards will be laid down for those in the hardware and software arena." == Usage Model Roadmap == The organization sees a growing need for solutions developed in an open, industry-standard and multivendor fashion, and has thus created a usage model roadmap featuring 19 prioritized usage models. The usage models provide detailed requirements for data center and cloud solutions, and will include detailed technical documentation discussing the requirements for technology deployments. To further its roadmap development, the steering committee established five initial technical workgroups in the areas of infrastructure, management, regulation & ecosystem, security and services. The organization delivered a 0.50 usage model roadmap to Open Data Center Alliance technical workgroups in Oct. 2010, and delivered a full 1.0 roadmap for public use in June 2011. == Membership == The steering committee consists of BMW, Capgemini, China Life, China Unicom Group, Deutsche Bank, JPMorgan Chase, Lockheed Martin, Marriott International, Inc., National Australia Bank, Royal Dutch Shell, Terremark and UBS. Other members include AT&T, CERN, eBay, Logica, Motorola Mobility Inc. and Nokia. "The demands on the IT organisations are coming at such an alarming rate that there are many, many different solutions being developed today that maybe don't work with each other. We need one voice, one road map, so that companies are able to say to manufacturers here is a clear vision of what they should be developing their product to do." says Marvin Wheeler, of Terremark, chairman of the Alliance. "While it's unclear how successful this alliance will be, it is at least shedding the spotlight on cloud interoperability, a big emerging issue," said Larry Dignan of ZDNet.

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  SWILE

  SWILE (formerly: Lunchr) is a French app-based company that focuses on improving the employee experience. Among others, the platform offers meal vouchers, gift vouchers, mobility vouchers, and business travel solutions. In March 2020, it was renamed SWILE and entered the lunch break and meal voucher market. == History == The company was founded as Lunchr by Loïc Soubeyrand in 2016. Originally, Lunchr was an app for pre-ordering lunch on the spot or to go. In January 2017, the company raised €2.5 million in seed funding from Daphni. In 2018, the company raised €11 million (series A) from Idinvest, followed by another €30 million in February 2019 (series B), notably from Index Ventures and Kima Ventures. In January 2020, Lunchr became one of the first startups to join the French Tech 120. A few months later, in March, Lunchr diversified its services, adding team life management tools and changing its brand name to Swile. In June 2020, the company raised €70 million more in a new round of financing (Series C) from the same investors and the BPI. In November 2020, Swile acquired Briq, a startup specializing in employee engagement. In January 2021, Swile won a tender with Carrefour and distributed 62,000 Swile cards to its employees. In early October 2021, a new $200 million (€175 million) fundraising round, in which Japanese Softbank joined other investors, allowed Swile to capitalize on $1 billion. President Emmanuel Macron cited the company as "a further proof that FrenchTech is at the forefront internationally." In May 2022, the company acquired the travel management start-up Okarito for €6 million. == Overview == Swile operates in two countries (France and Brazil) and has a total of 1000 employees, 5.5 million users and 85,000 corporate customers, including Carrefour, Le Monde, JCDECAUX, PSG, Airbnb, Spotify, Red Bull, and TikTok in the private sector, as well as numerous local authorities and ministerial references in the public sector.

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  Splitwise

  Splitwise is an online expense-splitting application software accessible via web browser and mobile app. The app facilitates repayments of shared bills by calculating what each person in a group owes. The primary competitor to the app is Venmo, which only operates in the U.S. Splitwise allows users to create groups with friends to determine what each person owes. All expenses and allocations are added to the app, and Splitwise simplifies the transaction history to determine exactly what payments need to be made to whom to settle outstanding balances. Splitwise stores user information via cloud storage. It was developed and is owned by Splitwise Inc., based in Providence, Rhode Island, United States. == History == The app was launched in February 2011 as SplitTheRent, intended to be used for rent splitting, by Ryan Laughlin, Jon Bittner and Marshall Weir. In September 2013, Splitwise was integrated with Venmo to allow users to settle payments via Venmo. In April 2024, Splitwise partnered with Tink, a Visa payment services company, to incorporate a bank transfer feature directly in the Splitwise app. === Financing === In December 2014, the company raised $1.4 million. In October 2016, the company raised $5 million. In April 2021, Splitwise raised $20 million in funding from series A round run by Insight Partners. == Reception == A 2022 opinion piece in The Guardian by London journalist Imogen West-Knights shared the negative effects of exactly splitting bills among friends and family members. West-Knights argued that Splitwise and similar apps can "turn people into those true enemies of all that is fun and joyful in the world: accountants." However, she said the app does work better when used by couples rather than friend groups. Other reviews noted that the app makes people petty. In contrast, an article published by Condé Nast Traveler describes how Splitwise eliminated stress caused by complicated offline bill splitting, saying it "fixed such a pervasive obstacle in group travel." Coverage by The Wall Street Journal lands somewhere in between the two contrasting views, saying Splitwise and similar apps are helpful, but users need to be prepared for difficult money-related conversations that may arise. An etiquette advisor at Debrett's, said, "The less talk you can have about money on any of these occasions, the better." An editor suggested conversations as simple as asking, "We’re splitting this evenly, right?" before a meal.

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  Elasticity (computing)

  In computing, elasticity is defined as "the degree to which a system is able to adapt to workload changes by provisioning and de-provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible". Elasticity is a defining characteristic that differentiates cloud computing from previously proposed distributed computing paradigms, such as grid computing. The dynamic adaptation of capacity, e.g., by altering the use of computing resources, to meet a varying workload is called "elastic computing". In the world of distributed systems, there are several definitions according to the authors; some consider the concepts of scalability a sub-part of elasticity, others as being distinct. == Purpose == Elasticity aims to match the amount of resources allocated to a service with the amount of resources it actually requires, avoiding over- or under-provisioning. Over-provisioning, i.e., allocating more resources than required, should be avoided as it may incur extra costs (monetary, energy, operational, etc.) for unused or underutilized resources. For example, if a website is over-provisioned with two cloud computing resources to handle current demand that only requires one resource, the costs of maintaining the second resource would effectively be wasted. Under-provisioning, i.e., allocating fewer resources than required, must be avoided; otherwise, the service cannot serve its users with a good service. For example, under-provisioning a website may make it seem slow or unreachable, because not enough resources have been allocated to meet current demand. == Example == Elasticity can be illustrated through an example of a service provider who wants to run a website on the cloud. At moment t 0 {\displaystyle t_{0}} , the website is unpopular and a single machine is sufficient to serve all users. At moment t 1 {\displaystyle t_{1}} , the website suddenly becomes popular, and a single machine is no longer sufficient to serve all users. Based on the number of web users simultaneously accessing the website and the resource requirements of the web server, ten machines are needed. An elastic system should immediately detect this condition and provision nine additional machines from the cloud to serve all users responsively. At time t 2 {\displaystyle t_{2}} , the website becomes unpopular again. The ten machines currently allocated to the website are mostly idle and a single machine would be sufficient to serve the few users who are accessing the website. An elastic system should immediately detect this condition and deprovision nine machines, releasing them to the cloud. == Problems == === Resource provisioning time === Resource provisioning takes time. A cloud virtual machine (VM) can be acquired at any time by the user; however, it may take up to several minutes for the acquired VM to be ready to use. The VM startup time is dependent on factors such as image size, VM type, data center location, number of VMs, etc. Cloud providers have different VM startup performance. This implies that any control mechanism designed for elastic applications must consider the time needed for the resource provisioning actions to take effect. === Monitoring elastic applications === Elastic applications can allocate and deallocate resources on demand for specific application components. This makes cloud resources volatile, and traditional monitoring tools which associate monitoring data with a particular resource, such as Ganglia or Nagios, are no longer suitable for monitoring the behavior of elastic applications. For example, during its lifetime, a data storage tier of an elastic application might add and remove data storage VMs due to cost and performance requirements, varying the number of used VMs. Thus, additional information is needed in monitoring elastic applications, such as associating the logical application structure over the underlying virtual infrastructure. This in turn generates other problems, such as data aggregation from multiple VMs towards extracting the behavior of the application component running on top of those VMs, as different metrics may need to be aggregated differently (e.g., CPU usage could be averaged, network transfer might be summed up). === Stakeholder requirements === When deploying applications in cloud infrastructures (IaaS/PaaS), stakeholder requirements need to be considered in order to ensure that elastic behavior meets stakeholder needs. Traditionally, the optimal trade-off between cost and quality or performance is considered; however, for real world cloud users, requirements regarding elastic behavior are more complex and target multiple dimensions of elasticity (e.g., SYBL). === Multiple levels of control === Cloud applications vary in type and complexity, with multiple levels of artifacts deployed in layers. Controlling such structures must take into consideration a variety of issues. For multi-level control, control systems need to consider the impact lower level control has upon higher level ones, and vice versa (e.g., controlling virtual machines, web containers, or web services in the same time), as well as conflicts that may appear between various control strategies from various levels. Elastic strategies on in cloud computing can take advantage of control-theoretic methods (e.g., predictive control has been experimented in cloud computing scenarios by showing considerable advantages with respect to reactive methods). One approach to multi-level elastic clouc control is rSYBL.

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  List of Ruby software and tools

  This is a list of software and programming tools for the Ruby programming language, which includes libraries, web frameworks, implementations, tools, and related projects. == Web tools == Capistrano (software) – remote server automation tool Mongrel – Ruby web server Rack – interface between web servers and web applications Ruby on Rails – full-stack web application framework Sinatra – lightweight Ruby web application framework Spree Commerce – e-commerce platform WEBrick – Ruby HTTP server toolkit == Libraries == BioRuby – bioinformatics and computational biology library for Ruby Bogus – Ruby library for creating reliable test doubles with contract verification ERuby – embedded Ruby templating EventMachine – event-driven I/O library Factory Bot – test fixtures library Fat comma – Ruby library for JSON-like hash syntax Geocoder – Ruby library for geocoding and reverse geocoding addresses Haml – HTML templating engine Markaby – HTML generation via Ruby Nokogiri – XML/HTML parsing library RSpec – behavior-driven testing framework for Ruby RubyGems – package manager for Ruby libraries and applications Sass – CSS preprocessor Sidekiq – background job framework for Ruby, used to handle asynchronous tasks. Uconv – Unicode text conversion library Watir – web application testing framework == Ruby implementations == HotRuby – Ruby interpreter implemented in JavaScript, enabling Ruby code to run in web browsers. IronRuby – Ruby for .NET platform JRuby – Ruby on the Java Virtual Machine MacRuby – Ruby implementation for macOS Mod ruby – Apache module that embeds the Ruby interpreter to improve performance of Ruby web applications Mruby – lightweight Ruby interpreter Rubinius – alternative Ruby implementation, based loosely on the Smalltalk-80 Blue Book design. Ruby MRI – the standard Ruby interpreter YARV – "Yet Another Ruby VM," the bytecode interpreter used in modern Ruby implementations == Tools == Homebrew – package manager for macOS and Linux written in Ruby Pry – interactive Ruby shell Rake – build and task management Ruby Version Manager – environment manager RubyCocoa – bridge between Ruby and Cocoa RubyForge – project hosting site RubyMotion – for iOS/macOS development RubySpec – language specification tests == Integrated Development Environments == Aptana Studio — integrated RadRails plugin for Ruby on Rails development Eclipse DLTK Ruby Plugin — Ruby development plugin for Eclipse Eric — open-source Python-based IDE with Ruby support Komodo IDE — commercial cross-platform IDE with Ruby support RubyMine — commercial IDE for Ruby and Rails by JetBrains SlickEdit — commercial cross-platform IDE with Ruby support == List of websites using Ruby on Rails == Airbnb Basecamp Diaspora – decentralized social network application built with Ruby on Rails Discourse – open-source discussion platform built with Ruby on Rails Fiverr GitHub Hulu Shopify SoundCloud Twitch Zendesk

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  Caspio

  Caspio, Inc. is an American software company providing a low-code platform for building cloud-based business applications. Founded in 2000 by Frank Zamani, the company is headquartered in Sunnyvale, California, with operations in Poland, the Philippines, and Spain. Caspio’s platform allows organizations to create online database applications and workflow tools without extensive coding. == History == Caspio was founded by Frank Zamani in 2000. The company initially focused on simplifying custom cloud applications and reducing development time and cost as compared to traditional software development. Caspio released the first version of its platform, Caspio Bridge, in 2001. In 2014, Caspio released a HIPAA-Compliant Edition of its low-code application development platform. Caspio also released an EU General Data Protection Regulation (GDPR) Compliance Edition of its low-code application development platform in 2016. Caspio's second European Software Development Center opened in Kraków, Poland in 2017. In 2019, Forrester Research listed Caspio and three other platforms in its highest of four ranked tiers of twelve low-code platforms for business developers based on rankings of offerings and strategy at that time. Caspio also opened data centers in Montreal, Canada and India in 2020.

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  Web Dynpro

  Web Dynpro (WD) is a web application technology developed by SAP SE that focuses on the development of server-side business applications. For modern releases (for instance as of NetWeaver 750, software layer SAP_UI) the user interface is rendered according to the HTML5 web standard. Since Netweaver 754 (software layer SAP_UI, ABAP Platform 1909) a touch enabled user interface is available. The newly released versions usually follow the SAP Fiori design principles. One of its main design features is that the user interface is defined in an entirely declarative manner. Web Dynpro applications can be developed using either the Java (Web Dynpro for Java, WDJ or WD4J) or ABAP (Web Dynpro ABAP, WDA or WD4A) development infrastructure. == Overview == The earliest version of Web Dynpro appeared in 2003 and was based on Java. This variant was released approximately 18 months before the ABAP variant. As of 2010, the Java variant of Web Dynpro was put into maintenance mode. WD follows a design architecture based on an interpretation of the MVC design pattern and uses a model driven development approach ("minimize coding, maximize design"). The Web Dynpro Framework is a server-side runtime environment into which many dedicated "hook methods" are available. The developer then places their own custom coding within these hook methods in order to implement the desired business functionality. These hook methods belong to one of the broad categories of either "life-cycle" and "round-trip"; that is, those methods that are concerned with the life-cycle of a software component (i.e. processing that takes place at start up and shut down etc.), and those methods that are concerned with processing the fixed sequence of events that take place during a client-initiated round trip to the server. Web Dynpro is aimed at the development of business applications that follow standardized UI principles, applications that connect to backend systems and which are scalable. Key Capabilities Declarative way of development: Web Dynpro offers a graphical and declarative means of UI development. UI controls, building blocks, views and windows are modeled, and the business logic can be coded separately. Separation of user interface and business logic: One advantage of Web Dynpro over SAP GUI is the separation between business logic and user interface, and the structured development process with less implementation effort. Support of stateful application: The state of the application is kept in the back-end. This leads to a reduced data transfer from ABAP server to browser and vice versa. Regarding Web Dynpro ABAP there is only one programming language (ABAP) and only one system necessary. Therefore, development can be easier and cost efficient.

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  Collabora Online

  Collabora Online (often abbreviated as COOL) is an open-source online office suite developed by Collabora, based on LibreOffice Online, the web-based edition of the LibreOffice office suite. It enables real-time collaborative editing of documents, spreadsheets, presentations, and vector graphics in a web browser. Optional applications are available for offline use on Android, ChromeOS, iOS, iPadOS, Linux distributions, macOS, and Windows. It supports the OpenDocument format and is compatible with other major formats, including those used by Microsoft Office. The Document Foundation (TDF), the nonprofit organization behind LibreOffice, states that a majority of the LibreOffice software development is done by its partners like Collabora. Collabora Online is an open-source alternative to proprietary cloud office platforms such as Google Workspace and Microsoft 365. Unlike these services, it can be self-hosted or hosted by third-party providers. The platform is marketed particularly toward enterprises and public institutions seeking greater digital sovereignty and independence from U.S.-based "big tech" companies. Collabora also develops Collabora Office, a standalone desktop and mobile app suite based on LibreOffice. Although Collabora Online has increasingly taken on a central role, both products may be used in parallel, similar to Microsoft Office and Microsoft 365. In November 2025, Collabora released Collabora Office Desktop and renamed the previous product Collabora Office Classic. The new product shares code with Collabora Online and brings the same user interface to the desktop on Linux, Windows and MacOS. A separate version, the Collabora Online Development Edition (CODE), is offered free of charge and is recommended for individuals, small teams, and developers. CODE provides early access to new features and serves as a testing and development platform for open-source community contributors. As TDF does not offer a free version of LibreOffice Online, CODE represents the primary freely available option for organizations and individuals interested in deploying LibreOffice in a web-based, collaborative setting. == Applications == Collabora Online includes several applications for document editing, available through the web-based interface and optional desktop and mobile apps: Collabora Writer – A word processor based on LibreOffice Writer, comparable to Microsoft Word and Google Docs. It supports WYSIWYG editing, styles, formatting tools, comment threads, and change tracking. Collabora Calc – A spreadsheet editor based on LibreOffice Calc, similar to Microsoft Excel and Google Sheets. Features include pivot tables, formulas, data validation, conditional formatting, advanced sorting and filtering, charts, and support for up to 16,000 columns. Compatible with some macros written in VBA. Collabora Impress – A presentation program based on LibreOffice Impress, comparable to Microsoft PowerPoint and Google Slides. It supports master slides, transitions, speaker notes, and multimedia elements. Collabora Draw is not a separate application, most of the functionality of the Draw application is now integrated in Writer and Impress – vector graphics editor based on LibreOffice Draw, comparable to Microsoft Visio and Google Drawings. == Features == Collabora Online can be accessed from modern web browsers without the need for plug-ins or add-ons. It supports real-time collaborative editing of word processing documents, spreadsheets, presentations, and vector graphics. Collaboration features include commenting, version tracking with document comparison and restoration, and integration with communication tools such as chat or video calls. These functions are often enabled through integration with enterprise open-source cloud platforms like Nextcloud, ownCloud, Seafile, EGroupware, GroupOffice and others. Collabora Online can also be embedded or integrated into a variety of third-party applications. Although client apps are not required to use the web-based suite, optional applications are available for offline use on Android, ChromeOS, iOS, iPadOS, Linux distributions, macOS, and Windows. These apps share the same LibreOffice-based core as the server version, ensuring document compatibility across platforms. Development of the LibreOffice core benefits both the online server and the client applications simultaneously. The mobile apps offer touch-optimized interfaces that adapt to different screen sizes and can be used offline, with optional integration into cloud storage services. Collabora Online supports OpenDocument formats (ODF; .odt, .odp, .ods, .odg) in accordance with ISO/IEC 26300. It is also compatible with Microsoft Office formats, including Office Open XML (.docx, .pptx, .xlsx) and legacy binary formats (.doc, .ppt, .xls). Additional supported formats include PDF, PNG, CSV, TSV, RTF, EPUB, and others. The suite can import a range of formats supported by LibreOffice, including Microsoft Visio and Publisher files, Apple Keynote, Numbers, and Pages files, as well as legacy formats used by Lotus 1-2-3, Microsoft Works, and Quattro Pro. The core of Collabora Online is written in C++ and utilizes LibreOfficeKit, a programming interface that enables reuse of much of LibreOffice's existing code for document saving, loading, and rendering. Collabora Online operates on the principle that documents remain on the server, with users viewing tile-rendered images of the document and sending their edits back to the server. The user interface is implemented in JavaScript. For file access and authentication with file hosting services, Collabora Online uses Microsoft's WOPI protocol, allowing compatibility with any service supporting Microsoft 365 integration. == Server == The server component can be self-hosted or deployed through third-party enterprise open-source cloud platforms, allowing organizations to maintain control over data and infrastructure. It is available for various Linux distributions and as a Docker image. The server enables features such as in-browser document editing, file synchronization, and real-time communication. These third-party cloud platforms typically offer additional functionality comparable to services such as Dropbox, Google Workspace, Microsoft 365, or Zoom, including file sharing, calendars, email, contacts, chat, and video conferencing. Collabora Online can be integrated into these applications, as well as with other services such as learning management systems and enterprise content platforms, through open APIs and an SDK. == Reception == Various online and print publications have discussed Collabora Online. In December 2016 the technology website Softpedia mentioned the availability of collaborative editing in version 2.0 and the integration with ownCloud, Nextcloud, and other file synchronization and sharing solutions. In June 2020, ZDNET reported that Collabora Online would be included as the standard office suite in Nextcloud version 19, noting that direct document editing was added to the native video conferencing software Talk. The technology blog OMG! Ubuntu! covered the release of Collabora's Android and iOS apps, emphasizing their offline functionality. In September 2020, Linux Magazine compared Collabora Online with OnlyOffice, noting the flexibility and platform independence of both tools and highlighting Collabora's extensive feature set derived from LibreOffice. === Digital sovereignty === Collabora Online's open-source design and support for self-hosting have made it notable in discussions about digital sovereignty—the ability of users and organizations to control their own data. This is particularly relevant in Europe, where concerns about dependence on U.S.-based "big tech" companies and data privacy have grown in recent years. On 10th June 2025, Microsoft executives under oath in the French Senate admitted that they cannot guarantee data sovereignty and would be compelled to pass French (and by implication the wider European Union) information to the US administration if requested via a warrant or subpoena. The Cloud Act is a law that gives the US government authority to obtain digital data held by US-based tech corporations, irrespective of whether that data is stored on servers at home or on foreign soil. A 2020 briefing by the European Parliament highlighted risks associated with reliance on major technology companies that collect and exploit user data. Legal decisions such as the Schrems II ruling have further underscored these concerns. Several European government agencies have adopted private cloud solutions using Collabora Online and related platforms to enhance data security and maintain control over sensitive information. == History == The former LibreOffice development team from SUSE joined Collabora in September 2013, forming the subsidiary Collabora Productivity. In 2015 Collabora and IceWarp announced the development of an enterprise-ready version of LibreOffice Online to compete wi

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  Thai QR Payment

  Thai QR Payment or PromptPay (พร้อมเพย์) is a real-time payment system in Thailand that allows money transfers through digital channels using identifiers linked to a bank account, including a mobile phone number, citizen identification number, tax identification number or bank account number. The system was introduced in 2016 as part of Thailand's national e-payment infrastructure and was developed under the National e-Payment Master Plan, a government programme intended to expand digital payment infrastructure and reduce the use of cash in everyday transactions. It is owned by National ITMX ltd and Bank of Thailand and developed by Vocalink, a group by Mastercard == History == PromptPay (originally AnyID) is one of the National e-Payment projects and policies by Thailand, to regulate and standardize electronic payments to follow the technologies with internet and smartphones that is expanding and bringing technology into Finance and Commerce. By 22 December 2015, The First Prayut cabinet have approved the project as a national infastructure PromptPay has also been used in cross-border payment linkages with other real-time payment systems in Southeast Asia. In April 2021, the Monetary Authority of Singapore and the Bank of Thailand launched a linkage between Singapore's PayNow and Thailand's PromptPay, allowing customers of participating banks to send money between the two countries using a mobile phone number. In June 2021, the central banks of Thailand and Malaysia launched a cross-border QR payment linkage between PromptPay and Malaysia's DuitNow system. == Services == PromptPay's Services have included Encrypted Transactions and Payment between Two Individuals (C2C) Government Infrastructure Payment Tax Returns Individual PromptPay e-Wallet Thai QR Payment Pay Alert e-Donation Cross Border QR Payment

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  Server.com is a domain name that was owned by software as a service (SaaS) company Server Corporation. They offered a suite of services from 1996 until 2007. It was the first SaaS site to offer a variety of services and the first to use the term WebApp to describe its services. It was selected as an Incredibly Useful Site by Yahoo! Internet Life magazine. net magazine listed Server.com among the 100 most influential websites of all time. Server.com launched in 1996 offering the first online personal information manager. In 1997, they rolled out the first threaded message board service; the first web based mailing list manager; one of the first online calendar services; and one of the first online form builders. In 2000, Server.com partnered with NBCi and became server.snap.com until 2001. In 2001, Server.com was serving 100 million monthly pageviews. Media Life declared it one of the 20 biggest ad domains on the Web. In 2002, Server.com developed one of the first web-based RSS aggregators. In 2007, all services were moved to YourWebApps.com. The domain name Server.com was sold in 2009 for $770,000.

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