AI Grammar Summarizer

AI Grammar Summarizer — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Shaded Picture System

    Shaded Picture System

    The Shaded Picture System was a 3D raster computer display processor introduced by Evans & Sutherland in October 1973. The Shaded Picture System was the first general-purpose, commercially available raster computer graphics display processor capable of real-time, shaded 3D graphics. It could only display black and white graphics at a resolution of 256 by 256. It was extremely expensive, and very few units were ever sold. == History == The principles of shaded, hidden-line true 3D graphics were pioneered at the University of Utah in 1967. However, this algorithm was slow and would take several minutes to produce an image. In 1970, Gary Watkins developed a FORTRAN simulator of a faster algorithm that would theoretically generate shaded 3D images in real-time, "if implemented in suitable hardware". The simulator itself was still not capable of real-time shaded 3D image rendering. Evans & Sutherland developed a functional prototype of this "suitable hardware", which was later sold as the Shaded Picture System in 1973. About a year earlier in 1972, Evans & Sutherland sold the first and only CT1 to Case Western Reserve University. The CT1, or Continuous Tone 1, was a specialized image generator, not meant as a marketable or mass-produced product. At the time, the CT1, along with G.E./NASA's upgraded Electronic Scene Generator from 1971, would have been the only real-time raster graphics systems sold to customers comparable to the Shaded Picture System, although both the CT1 and Electronic Scene Generator were intentionally produced as one-off products and specialized for the needs of their customers. The Shaded Picture System, in contrast, was intentionally marketed.In early 1975, Evans & Sutherland demonstrated a random-access video frame buffer using relatively low-cost semiconductor memory, which was much more capable than the Shaded Picture System. When interfaced with a (non-shaded) E&S Picture System, the frame buffer had a resolution of 512 by 512 in grayscale and partial color capabilities. By the end of 1975, this frame buffer was commercially available.

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  • Azure Data Lake

    Azure Data Lake

    Azure Data Lake is a scalable data storage and analytics service. The service is hosted in Azure, Microsoft's public cloud. == History == Azure Data Lake service was released on November 16, 2016. It is based on COSMOS, which is used to store and process data for applications such as Azure, AdCenter, Bing, MSN, Skype and Windows Live. COSMOS features a SQL-like query engine called SCOPE upon which U-SQL was built. == Storage == Data Lake Storage is a cloud service to store structured, semi-structured or unstructured data produced from applications including social networks, relational data, sensors, videos, web apps, mobile or desktop devices. A single account can store trillions of files where a single file can be greater than a petabyte in size. == Analytics == Data Lake Analytics is a parallel on-demand job service. The parallel processing system is based on Microsoft Dryad. Dryad can represent arbitrary Directed Acyclic Graphs (DAGs) of computation. Data Lake Analytics provides a distributed infrastructure that can dynamically allocate resources so that customers pay for only the services they use. The system uses Apache YARN, the part of Apache Hadoop which governs resource management across clusters. Data Lake Store supports any application that uses the Hadoop Distributed File System (HDFS) interface. == U-SQL == U-SQL is a query language for Data Lake Analytics parallel data transformation and processing programs. It combines SQL and C#: it is and an evolution of the declarative SQL language with native extensibility through user code written in C#. U-SQL uses C# data types and the C# expression language. == Retirement == In 2021, Microsoft announced the 2024 retirement of the original Azure Data Lake Storage, now called "Gen1". The related Azure Data Lake Analytics / U-SQL technologies are also being retired. Azure Data Lake Storage Gen2, an extension of Azure Storage, will continue. The suggested replacement technologies are Azure Synapse Analytics and Apache Spark.

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  • FreshBooks

    FreshBooks

    FreshBooks is accounting software operated by 2ndSite Inc. primarily for small and medium-sized businesses. It is a web-based software as a service (SaaS) model, that can be accessed through a desktop or mobile device. The company was founded in 2003 and is based in Toronto, Canada. == History == FreshBooks was founded in 2004 by Mike McDerment, Levi Cooperman, and Joe Sawada in Toronto, Ontario. McDerment incorporated a second company, BillSpring in January 2015 to work on new product development. It was rolled back into FreshBooks as an updated interface in 2016. Initially FreshBooks functioned like an electronic invoicing program targeting IT professionals. After the release of the new interface, the initial release of FreshBooks was referred to as "FreshBooks Classic." FreshBooks Classic was discontinued in 2022 after migrating users to the new platform. FreshBooks Classic's front-end application was built in PHP, and the backend services were built in Python while the new FreshBooks uses the same backend services with a JavaScript single-page application. == Product == FreshBooks is a subscription-based accounting software platform that provides features such as invoicing, accounts payable, expense and time tracking, retainers, fixed asset depreciation, purchase orders, payroll integrations, mileage tracking, double-entry accounting, and standard business reporting. Financial data is stored in the cloud on a unified ledger, enabling access from desktop and mobile devices. The platform includes a free API for integration with external applications and supports multiple tax rates and currencies. It also offers project management and payroll functionalities. Pricing is based on a recurring monthly fee. FreshBooks supports country-specific tax calculations, including GST and HST in Canada, sales taxes in the United States, and MTD compliance in the UK. == Operations == FreshBooks has its headquarters in Toronto, Canada with operations in North America, Europe and Australia. Founder Mike McDerment was the chief executive officer of the company from 2003 until 2021, when he stepped down and was replaced by Don Epperson, but stayed as the executive chair. Don Epperson had previously joined FreshBooks as executive director in 2019. == Funding == FreshBooks was initially self-funded. In 2014, the company raised a Series A venture investment of $30 million led by the venture capital firm Oak Investment Partners, with participation by Georgian Partners and Atlas Venture. In 2017, FreshBooks announced that it raised another $43 million in funding from Accomplice, Georgian Partners and Oak Investment Partners. On August 10, 2021, FreshBooks announced that it had secured $80.75 million in Series E funding and $50 million in debt financing. FreshBooks also reached a valuation of more than $1 billion.

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  • NCover

    NCover

    NCover is a .NET code coverage tool. There are two non-related NCover products that do .NET code coverage. There is an open source NCover that can be found on SourceForge and there is a company called NCover, LLC. There has been additional development on both products since this 2004 reference. The company NCover, LLC began when the founder, Peter Waldschmidt, decided to commercialize the open source tool he created. The commercial versions were launched in 2007, but the last supported free version 1.5.8 is still available on the company site.

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  • Automated dispensing cabinet

    Automated dispensing cabinet

    An automated dispensing cabinet (ADC), also called a unit-based cabinet (UBC), automated dispensing device (ADD), or automated dispensing machine (ADM)[1], is a computerized medicine cabinet for hospitals and healthcare settings. ADCs allow medications to be stored and dispensed near the point of care while controlling and tracking drug distribution. == Overview == Hospital pharmacies have provided medications for patients by filling patient-specific cassettes of unit-dose medications that were then delivered to the nursing unit and stored in medication cabinets or carts. ADCs, originally designed for hospital use, were introduced in hospitals in the 1980s and have facilitated the transition to alternative delivery models and more decentralized medication distribution systems.[2] Implementing automated dispensing cabinets as part of a decentralized or hybrid medication distribution system can improve patient safety and the accountability of the inventory, streamline certain billing processes. However, in the 2000s, the technology began to be deployed into other care settings where medication doses were stored onsite, and higher security methods were needed to control inventory, access, and dispensing of each patient dose. Settings that now deploy ADCs include long-term care facilities, hospice, critical access hospitals, surgery centers, group homes, residential care facilities, rehab and psych environments, animal health, dental clinics, and nursing education simulation. These diverse care settings share a common need to safely store, account for, and dispense individual doses of medications, especially narcotics and high-value medications, at the point of care.[3] ADCs track user access and dispensed medications, and their use can improve control over medication inventory. The real-time inventory reports generated by many cabinets can simplify the filling process and help the pharmacy track expired drugs. Furthermore, by restricting individual drugs – such as high-risk medications and controlled substances – to unique drawers within the cabinet, overall inventory management, patient safety, and medication security can be improved. Automated dispensing cabinets allow the pharmacy department to profile physician orders before they are dispensed.[4] ADCs can also enable providers to record medication charges upon dispensing, reducing the billing paperwork the pharmacy is responsible for. In addition, nurses can note returned medications using the cabinets' computers, enabling direct credits to patients' accounts. Since automated cabinets can be located on the nursing unit floor, nursing have speedier access to a patient's medications. Also, shorter waiting time ensures improved patient comfort and care.[5] == Role of automated dispensing in healthcare == Automated dispensing is a pharmacy practice in which a device dispenses medications and fills prescriptions. ADCs, which can handle many different medications, are available from a number of manufacturers such as BD, ARxIUM, and Omnicell. Though members of the pharmacy community have been utilizing automation technology since the 1980s, companies are constantly improving ADCs to meet changing needs and health standards in the industry. Several goals can be met by implementing an automated product in a healthcare facility. Patient safety can be ensured with the use of ADC technology such as barcoding. Anesthesia ADCs in operating rooms and perioperative areas may include label printing to prevent mix-ups such as errors between morphine and hydromorphone, two different opioid analgesics that frequently get confused. These systems also communicate with the pharmacy and its information management system to track medications removed and support inventory replenishment. == Key features == ADCs are like automated teller machines whose specific technologies such as barcode scanning and clinical decision support can improve medication safety. Some have metal locking drawers for added security and some have automated single-dose dispensing to prevent the need for a blind count each time a controlled substance is accessed. Over the years, ADCs have been adapted to facilitate compliance with emerging regulatory requirements such as pharmacy review of medication orders and safe practice recommendations. ADCs incorporate advanced software and electronic interfaces to synthesize high-risk steps in the medication use process. These unit-based medication repositories provide computer-controlled storage, dispensation, tracking, and documentation of medication distribution in the resident care unit. Since automated dispensing cabinets are not located in the pharmacy, they are considered "decentralized" medication distribution systems. Instead, they can be found at the point of care on the resident care unit. Tracking of the stocking and distribution process can occur by interfacing the unit with a central pharmacy computer. These cabinets can also be interfaced with other external databases such as resident profiles, the facility's admission/discharge/transfer system, and billing systems. Most ADC providers offer scalable systems since several important factors vary widely by facility such as budget, physical room size, patient population/demographics, type of healthcare facility, etc.

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  • Identi.ca

    Identi.ca

    identi.ca is a free and open-source social networking and blogging service based on the pump.io software, using the Activity Streams protocol. Identi.ca stopped accepting new registrations in 2013, but continues to operate alongside several other pump.io-based hosts provided by E14N which continue to accept new registrations. == Features == Identi.ca is similar to social networking sites like Facebook and Google+, allowing unlimited length status updates, rich text, and images. The Activity Streams protocol supports many kinds of activities such as games. OpenFarmGame is a prototype application for an Activity Streams-based game. Previous features from its StatusNet version such as hashtags, groups, and global search are not supported. == History == === StatusNet === The service received more than 8,000 registrations and 19,000 updates within the first 24 hours of publicly launching on July 2, 2008, and reached its 1,000,000th notice on November 4, 2008. In January 2009, identi.ca received investment funds from venture capital group Montreal Start Up. On March 30, 2009, Control Yourself (since renamed StatusNet Inc) announced that Identi.ca was to become part of a hosted microblogging service called status.net to be launched in May 2009. Status.net offers individual microblogs under a subdomain to be chosen by the customer. Identi.ca will remain a free service. All notices will be published under the Creative Commons Attribution 3.0 license by default, but paying customers will be free to choose a different license. Formerly based on StatusNet, a micro-blogging software package built on the OStatus specification (and earlier based on the OpenMicroBlogging specification), Identi.ca allowed users to send text updates (known as "notices") up to 140 characters long. While similar to Twitter in both concept and operation, Identi.ca/StatusNet provided many features not currently implemented by Twitter, including XMPP support and personal tag clouds. In addition, Identi.ca/StatusNet allowed free export and exchange of personal and "friend" data based on the FOAF standard; therefore, notices could be fed into a Twitter account or other service, and also ported in to a private system similar to Yammer. === pump.io === Developer Evan Prodromou chose to change the site to the pump.io software platform in development, because pump.io offers more features making it technically more advanced. Registration on Identi.ca was closed in December 2012 in preparation for the switch to pump.io software (the popularity of Identi.ca and "official" Status.net hosting were considered a hindrance to the creation of a federated social network). The conversion was completed on 12 July 2013. The 140 character per post limit was removed (in StatusNet, it was a setting, not an inherent limitation); now the blog posts can contain formatting and images. Groups, hashtags, and a page listing popular posts are not yet implemented in pump.io.

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  • Directional cubic convolution interpolation

    Directional cubic convolution interpolation

    Directional cubic convolution interpolation (DCCI) is an edge-directed image scaling algorithm created by Dengwen Zhou and Xiaoliu Shen. By taking into account the edges in an image, this scaling algorithm reduces artifacts common to other image scaling algorithms. For example, staircase artifacts on diagonal lines and curves are eliminated. The algorithm resizes an image to 2x its original dimensions, minus 1.

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  • MyPoolin

    MyPoolin

    Mypoolin is a mobile peer-to-peer and group payment application. Their software allows the settling of debts and group-expenditure for events and activities. The software utilizes Unified Payment Interface of India to collect and settle daily expenses with friends. Users can also plan and pay together for group-gifting, movies, vacations, concerts, events, and parties. == Service == Mypoolin is a mobile payment provider that lets its users transfer money to other users via their mobile number. A user can create an account by verifying an OTP code which is sent to his mobile phone. It also allows the users to track their friends’ activities on the app. == History == Mypoolin was founded by Rohit Taneja (IIT Delhi) and Ankit Singh (FMS Delhi) in 2014 as a medium to aggregate money for various purposes in a hassle free and quick manner. Prior to the mobile app launch, Mypoolin was initially launched as a web application. == Funding == Mypoolin has been seed funded by angel investors. As winners of the QPrize 2015, Mypoolin jointly received an additional funding of $250,000 from Qualcomm Ventures. == Growth == Mypoolin reached INR 10 lakhs in revenue during its first four months of the web application launch, and was listed in the "Top ten free apps" in its category within the first 5 days of the Android app launch. It was one of the Top 50 start-ups in Asia at the Echelon Asia Summit held in Singapore. And among the top 3 start-ups in 1776 Cup Challenge 2016. Apple Inc also featured the app on their app store in India. == Features == Users are able to collect and share money on the app for daily uses like movies, events and trips. The money collected can then be redeemed in the form of an online voucher redeemable across several e-commerce sites. The amount can be redeemed also in the form of an offline debit card delivered to the address or in the form of a wire transfer. == Media coverage == Mypoolin was featured in The Economic Times and The Hindu Business Line after winning the Qualcomm Ventures' QPrize 2015. Digit magazine featured them recently as the app of the week. The app has mostly grown organically so far in the Indian urban millennial space.

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  • C3D Toolkit

    C3D Toolkit

    C3D Toolkit is a proprietary cross-platform geometric modeling kit software developed by Russian C3D Labs (previously part of ASCON Group). It's written in C++ . It can be licensed by other companies for use in their 3D computer graphics software products. The most widely known software in which C3D Toolkit is typically used are computer aided design (CAD), computer-aided manufacturing (CAM), and computer-aided engineering (CAE) systems. C3D Toolkit provides routines for 3D modeling, 3D constraint solving, polygonal mesh-to-B-rep conversion, 3D visualization, and 3D file conversions etc. == History == Nikolai Golovanov is a graduate of the Mechanical Engineering department of Bauman Moscow State Technical University as a designer of space launch vehicles. Upon his graduation, he began with the Kolomna Engineering Design bureau, which at the time employed the future founders of ASCON, Alexander Golikov and Tatiana Yankina. While at the bureau, Dr Golovanov developed software for analyzing the strength and stability of shell structures. In 1989, Alexander Golikov and Tatiana Yankina left Kolomna to start up ASCON as a private company. Although they began with just an electronic drawing board, even then they were already conceiving the idea of three-dimensional parametric modeling. This radical concept eventually changed flat drawings into three-dimensional models. The ASCON founders shared their ideas with Nikolai Golovanov, and in 1996 he moved to take up his current position with ASCON. As of 2012 he was involved in developing algorithms for C3D Toolkit. In 2012 the earliest version of the C3D Modeller kernel was extracted from KOMPAS-3D CAD. It was later adopted to a range of different platforms and advertised as a separate product. == Overview == It incorporates five modules: C3D Modeler constructs geometric models, generates flat projections of models, performs triangulations, calculates the inertial characteristics of models, and determines whether collisions occur between the elements of models; C3D Modeler for ODA enables advanced 3D modeling operations through the ODA's standard "OdDb3DSolid" API from the Open Design Alliance; C3D Solver makes connections between the elements of geometric models, and considers the geometric constraints of models being edited; C3D B-Shaper converts polygonal models to boundary representation (B-rep) bodies; C3D Vision controls the quality of rendering for 3D models using mathematical apparatus and software, and the workstation hardware; C3D Converter reads and writes geometric models in a variety of standard exchange formats. == Features == == Development == == Applications == Since 2013 - the date the company started issuing a license for the toolkit -, several companies have adopted C3D software components for their products, users include: Recently, C3D Modeler has been adapted to ODA Platform. In April 2017, C3D Viewer was launched for end users. The application allows to read 3D models in common formats and write it to the C3D file format. Free version is available.

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  • Nextcloud

    Nextcloud

    Nextcloud is a modular workspace platform designed to provide teams and businesses with a comprehensive environment for digital collaboration. Beyond central data management, it integrates office suites like Collabora Online and EuroOffice office suites. for seamless, cooperative workflows. The platform features built-in tools for chat, videoconferencing, and a privacy-focused AI assistant capable of running entirely on local LLMs. Supported by a rich ecosystem of apps, it can be hosted in the cloud or on premises and can scale up to millions of users. It has been translated into over 100 languages. == Features == Nextcloud files are stored in conventional directory structures, accessible via WebDAV if necessary. A SQLite, MySQL/MariaDB or PostgreSQL database is required to provide additional functionality like permissions, shares, and comments. Nextcloud can synchronize with local clients running Windows (Windows 8.1 and above), macOS (10.14 or later), Linux and FreeBSD. Nextcloud permits user and group administration locally or via different backends like OpenID or LDAP. Content can be shared inside the system by defining granular read/write permissions between users and groups. Nextcloud users can create public URLs when sharing files. Logging of file-related actions, as well as disallowing access based on file access rules is also available. Security options like brute-force protection and multi-factor authentication using TOTP, WebAuthn, Oauth2, and OpenID Connect are available. Nextcloud has planned new features such as monitoring capabilities, full-text search and Kerberos authentication, as well as audio/video conferencing, expanded federation and smaller user interface improvements. == History == In April 2016 Frank Karlitschek and most core contributors left ownCloud Inc. These included some of ownCloud's staff according to sources near to the ownCloud community. Karlitschek and many of these contributors went on to fork ownCloud, creating Nextcloud. The fork was preceded by a blog post of Karlitschek announcing his departure and raising questions about the management of the ownCloud, its community, and priorities between growth, money, and sustainability. There have been no official statements about the reason for the fork. However, Karlitschek mentioned the fork several times in a talk at the 2018 FOSDEM conference and in two appearances on the FLOSS Weekly podcast, emphasizing cultural mismatch between open source developers and business oriented people not used to the open source community. On June 2, within 12 hours of the announcement of the fork, the American entity "ownCloud Inc." announced that it is shutting down with immediate effect, stating that "[...] main lenders in the US have cancelled our credit. Following American law, we are forced to close the doors of ownCloud, Inc. with immediate effect and terminate the contracts of 8 employees." ownCloud Inc. accused Karlitschek of poaching developers, while Nextcloud developers such as Arthur Schiwon stated that he "decided to quit because not everything in the ownCloud Inc. company world evolved as I imagined". ownCloud GmbH continued operations, secured financing from new investors and took over the business of ownCloud Inc. In April 2018 Informationstechnikzentrum Bund (ITZBund) reported Nextcloud won the tender for "Bundescloud" (Germany government cloud) project. In August 2019 it was announced that the governments of France, Sweden and the Netherlands would use Nextcloud for file transfer. In January 2020 Nextcloud 18 "Nextcloud Hub" was released. The major change was direct integration with an Office suite (OnlyOffice) and Nextcloud announced that their goal was to compete with Office 365 and Google Docs. A partnership with Ionos was revealed – its hosting location in Germany and compliance with GDPR should support the goal of data sovereignty. In spring 2020 remote work and web conferencing usage increased due to the COVID-19 pandemic and Nextcloud released version 19 with chat and videoconferencing Talk app integrated into the application core. Communication with an optional "high performance back-end" allows self-hosting of web conferences with more than 10 participants. Collabora Online was introduced as another integrated office suite. In August 2021 Nextcloud was chosen as a collaboration platform for European cloud software GAIA-X. In a September 2021 European Commission report it was mentioned as "the most widely deployed Open Source content collaboration platform" Following the 2025 United States tariffs against the European Union, fear of overreliance on US cloud providers such as Microsoft 365 and Google Workspace increased, with Nextcloud being one of the foremost contenders to replace them. Some governmental organisations including the European Data Protection Supervisor and the German state of Schleswig-Holstein have since switched from Microsoft's Sharepoint to Nextcloud. According to Nextcloud, during the first 5 months of 2025, customer interest in the software had tripled.

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  • FastTrack Automation Studio

    FastTrack Automation Studio

    FastTrack Automation Studio (formerly known as FastTrack Scripting Host), often referred to as just FastTrack, is a scripting language for Windows IT System Administrators. The product’s goal is to handle any kind of scripting that might be required to automate processes with Microsoft Windows networks. == Manufacturer == FastTrack is produced by FastTrack Software, which is headquartered in Aalborg, Denmark. The product is promoted by the manufacturer as a one-stop shop for Windows script writers and its development paradigm is “one operation = one script line”. Script writers use a purpose-built editor to create scripts, inserting script lines via menus, drag’n drop, or simply typing them in. Scripts may be used out of the box, created from scratch, imported from forums or other users, or customized from product documentation. == Types of scripts == Simple scripts include: Outlook Signatures Login scripts Backup and replication scripts Inventory and asset management Automated Windows OS installation and deployment Automated application software deployment Active Directory scripts More advanced scripts include: SCCM task sequences Citrix ICA and RDP Clients built-in Deploying applications to server farms Deploying GPO MSI files SQL Server scripts == Basic structure == Under the hood, scripts comprise commands, functions, collections, and conditions. When a script is executed these components are converted into many lines of C# code, sometimes hundreds of lines, depending on the particular script operation. Scripts can be compiled into EXE files or MSI packages and treated as standalone Windows applications. == History == FastTrack Scripting Host (FastTrack) was first developed around 2006 to ease the administration burden of IT System Administrators on Windows networks. === Product idea === The idea for the product came from founder and President of FastTrack Software, Lars Pedersen, who has a background in systems administration. Previously with Telenor, Denmark’s major telephone company, Pedersen performed various roles in systems administration, programming and web development. He also worked as a consultant and developer on several major projects at various companies in Europe. Dissatisfied from his own experiences and frustrations administering Windows networks, Pederson looked for a way to make life easier for system administrators. In particular, he wanted something that could minimize the amount of time needed each day to perform routine and mundane tasks, which was a waste of time and expertise that should have been committed to other projects. === Development === Leading a small team of developers, Pedersen developed FastTrack Scripting Host to simplify and automate the routine tasks of system administrators. The resulting product is definitely a scripting language, but it can be used intuitively like a programming language, without requiring users to learn syntax or other concepts typically associated with programming languages. === Marketing === In April 2010, FastTrack Software entered into an agreement with Binary Research International Archived 2008-10-15 at the Wayback Machine, based in the city of Milwaukee, United States to market and sell the product globally. === Awards === FSH received a Windows IT Pro Community Choice award in 2012. == Versions == The first version was produced in June 2006 and contained 51 components, which are the commands, functions, conditions and collections making up FastTrack. The following table summarizes dates and components for major releases. Companies and organizations such as NOAA, Kawasaki, and Goodyear have used and implemented the FastTrack Scripting Host. == Comparison with other scripting software == FastTrack Scripting Host Kixtart PowerShell ScriptLogic VBScript

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  • List of publications in data science

    List of publications in data science

    This is a list of publications in data science, generally organized by order of use in a data analysis workflow. See the list of publications in statistics for more research-based and fundamental publications; while this list is more applied, business oriented, and cross-disciplinary. General article inclusion criteria are: Papers from notable practitioners or notable professors, either with a Wikipedia page or reference to their notability Common knowledge all data professionals should know, with references validating this claim Highly cited applied statistics and machine learning publications Discussion-facilitating papers on the field of data science as a whole (for example, the Attention Is All You Need paper is arguably a landmark paper that can be added here, but it is specific to generative artificial intelligence, not for all practitioners of data) Some reasons why a particular publication might be regarded as important: Topic creator – A publication that created a new topic Breakthrough – A publication that changed scientific knowledge significantly Influence – A publication which has significantly influenced the world or has had a massive impact on the teaching of data science. When possible, a reference is used to validate the inclusion of the publication in this list. == History == Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) Author: Leo Breiman Publication data: Online version: https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.pdf Description: Describes two cultures of statistics, one using a parsimonious and generative stochastic model, while the other is an algorithmic model with no known mechanism for how the data is generated. Breiman argues that while statistics has traditionally favored using the stochastic model, there is value in expanding the methods that statisticians can use to study phenomenon. Importance: Influence on the philosophies of statisticians right before the increased use of machine learning and deep learning methods. In a 20-year retrospective on this article, "Breiman's words are perhaps more relevant than ever". Notable statisticians at the time wrote opinion pieces about the publication. Although overall critical of the publication, David Cox writes that the publication "contains enough truth and exposes enough weaknesses to be thought-provoking." Bradley Efron commented that this publication is a "stimulating paper". Emanuel Parzen also comments about this publication that "Breiman alerts us to systematic blunders (leading to wrong conclusions) that have been committed applying current statistical practice of data modeling". Data Scientist: The Sexiest Job of the 21st Century Author: Thomas H. Davenport and DJ Patil Publication data: Online version: hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century Description: Describes the new role at companies that is coined "Data scientist", what they do, how an organization might recruit one to their organization, and how to work with one effectively. Importance: This publication has been an influence on the data community as mentioned near the time it was published in 2012 by institutions like IEEE Spectrum, but also mentioned nearly a decade later asking the same question the title poses. In a retrospective response to their own publication 10 years earlier, authors Davenport and Patil have reflected that the role of a data scientist has "become better institutionalized, the scope of the job has been redefined, the technology it relies on has made huge strides, and the importance of non-technical expertise, such as ethics and change management, has grown". 50 Years of Data Science Author: David Donoho Publication data: Online version: https://www.tandfonline.com/doi/full/10.1080/10618600.2017.1384734 Description: Retrospective discussion paper on the history and origins of data science, with a number of commentary from notable statisticians. Importance: This has been described as "the first in the field to present such a comprehensive and in-depth survey and overview", and helps to define the field that has many definitions. The Composable Data Management System Manifesto Author: Pedro Pedreira, Orri Erling, Konstantinos Karanasos, Scott Schneider, Wes McKinney, Satya R Valluri, Mohamed Zait, Jacques Nadeau Publication data: Online version: https://www.vldb.org/pvldb/vol16/p2679-pedreira.pdf Description: The vision paper advocating for a paradigm shift in how data management systems are designed using standard, composable, interoperable tools rather than siloed software tools. Importance: A paradigm shifting view on how future data science software tools should be designed for more efficient workflows, the principles of which "will be especially crucial for addressing fragmentation, improving interoperability, and promoting user-centricity as data ecosystems grow increasingly complex". == Data collection and organization == Tidy Data Author: Hadley Wickham Publication data: Online version: https://www.jstatsoft.org/article/view/v059i10/ https://vita.had.co.nz/papers/tidy-data.pdf Description: Describes a framework for data cleaning that is summarized in the quote, "each variable is a column, each observation is a row, and each type of observational unit is a table". This allows a standard data structure for which data analysis tools can be consistently built around. Importance: Cited over 1,500 times, this effort for tidy data has been described by David Donoho as having "more impact on today's practice of data analysis than many highly regarded theoretical statistics articles". In the context of data visualization, this publication is said to support "efficient exploration and prototyping because variables can be assigned different roles in the plot without modifying anything about the original dataset". Data Organization in Spreadsheets Author: Karl W. Broman and Kara H. Woo Publication data: Online version: https://www.tandfonline.com/doi/full/10.1080/00031305.2017.1375989 Description: This article offers practical recommendations for organizing data in spreadsheets, like Microsoft Excel and Google Sheets, to reduce errors and lower the barrier for later analyses due to limitations in spreadsheets or quirks in the software. Importance: Influences teaching both data and non-data practitioners to create more analysis-friendly spreadsheets, and has been described to outline "spreadsheet best practices". == Data visualizations == Quantitative Graphics in Statistics: A Brief History Author: James R. Beniger and Dorothy L. Robyn Publication data: Online version: https://www.jstor.org/stable/2683467 Description: Outlines history and evolution of quantitative graphics in statistics, going through spatial organization (17th and 18th centuries), discrete comparison (18th and 19th centuries), continuous distribution (19th century), and multivariate distribution and correlation (late 19th and 20th centuries). Importance: Helps put into perspective for learning data practitioners the recency of graphics that are used. A later publication "Graphical Methods in Statistics" by Stephen Fienberg in 1979 writes that his publication "owes much to the work of Beniger and Robyn". == Practice == Data Science for Business Author: Foster Provost and Tom Fawcett Publication data: Online version: N/A Description: Broadly outlines principles of data science and data-analytic thinking for businesses. Importance: Cited over 3,000 times, it is "highly recommended for students" but also it is also recommended due to its "relevance to senior management leaders who want to build and lead a team of data scientists and implement data science in solving complex business problems". == Tooling == Hidden Technical Debt in Machine Learning Systems Author: D. Sculley, Gary Holy, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison Publication data: Online version: https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf Description: This paper argues that it is "dangerous to think of [complex machine learning] quick wins as coming for free" and overviews risk factors to account for when implementing a machine learning system. Importance: All authors worked for Google, article is cited over 2,000 times, and helped practitioners thinking about quickly implementing a machine learning tool without understanding the long-term maintenance of the tool. A few useful things to know about machine learning Author: Pedro Domingos Publication data: Online version: https://dl.acm.org/doi/10.1145/2347736.2347755 https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf Description: The purpose of this paper is to distill inaccessible "folk knowledge" to effectively implement machine learning projects because "machin

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  • Application performance engineering

    Application performance engineering

    Application performance engineering is a method to develop and test application performance in various settings, including mobile computing, the cloud, and conventional information technology (IT). == Methodology == According to the American National Institute of Standards and Technology, nearly four out of every five dollars spent on the total cost of ownership of an application is directly attributable to finding and fixing issues post-deployment. A full one-third of this cost could be avoided with better software testing. Application performance engineering attempts to test software before it is published. While practices vary among organizations, the method attempts to emulate the real-world conditions that software in development will confront, including network deployment and access by mobile devices. Techniques include network virtualization.

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  • Digital image

    Digital image

    A digital image is an image composed of picture elements, also known as pixels, each with finite, discrete quantities of numeric representation for its intensity or gray level that is an output from its two-dimensional functions fed as input by its spatial coordinates denoted with x, y on the x-axis and y-axis, respectively. An image can be vector or raster type. By itself, the term "digital image" usually refers to raster images or bitmapped images (as opposed to vector images). == Raster == Raster images have a finite set of digital values, called picture elements or pixels. The digital image contains a fixed number of rows and columns of pixels. Pixels are the smallest individual element in an image, holding quantized values that represent the brightness of a given color at any specific point. Typically, the pixels are stored in computer memory as a raster image or raster map, a two-dimensional array of small integers. These values are often transmitted or stored in a compressed form. Raster images can be created by a variety of input devices and techniques, such as digital cameras, scanners, coordinate-measuring machines, seismographic profiling, airborne radar, and more. They can also be synthesized from arbitrary non-image data, such as mathematical functions or three-dimensional geometric models; the latter being a major sub-area of computer graphics. The field of digital image processing is the study of algorithms for their transformation. === Raster file formats === Most users come into contact with raster images through digital cameras, which use any of several image file formats. Some digital cameras give access to almost all the data captured by the camera, using a raw image format. The Universal Photographic Imaging Guidelines (UPDIG) suggests these formats be used when possible since raw files produce the best quality images. These file formats allow the photographer and the processing agent the greatest level of control and accuracy for output. Their use is inhibited by the prevalence of proprietary information (trade secrets) for some camera makers, but there have been initiatives such as OpenRAW to influence manufacturers to release these records publicly. An alternative may be Digital Negative (DNG), a proprietary Adobe product described as "the public, archival format for digital camera raw data". Although this format is not yet universally accepted, support for the product is growing, and increasingly professional archivists and conservationists, working for respectable organizations, variously suggest or recommend DNG for archival purposes. == Vector == Vector images resulted from mathematical geometry (vector). In mathematical terms, a vector consists of both a magnitude, or length, and a direction. Often, both raster and vector elements will be combined in one image; for example, in the case of a billboard with text (vector) and photographs (raster). Example of vector file types are EPS, PDF, and AI. == Image viewing == Image viewer software displayed on images. Web browsers can display standard internet images formats including JPEG, GIF and PNG. Some can show SVG format which is a standard W3C format. In the past, when the Internet was still slow, it was common to provide "preview" images that would load and appear on the website before being replaced by the main image (to give a preliminary impression). Now Internet is fast enough and this preview image is seldom used. Some scientific images can be very large (for instance, the 46 gigapixel size image of the Milky Way, about 194 GB in size). Such images are difficult to download and are usually browsed online through more complex web interfaces. Some viewers offer a slideshow utility to display a sequence of images. == History == Early digital fax machines such as the Bartlane cable picture transmission system preceded digital cameras and computers by decades. The first picture to be scanned, stored, and recreated in digital pixels was displayed on the Standards Eastern Automatic Computer (SEAC) at NIST. The advancement of digital imagery continued in the early 1960s, alongside development of the space program and in medical research. Projects at the Jet Propulsion Laboratory, MIT, Bell Labs and the University of Maryland, among others, used digital images to advance satellite imagery, wirephoto standards conversion, medical imaging, videophone technology, character recognition, and photo enhancement. Rapid advances in digital imaging began with the introduction of MOS integrated circuits in the 1960s and microprocessors in the early 1970s, alongside progress in related computer memory storage, display technologies, and data compression algorithms. The invention of computerized axial tomography (CAT scanning), using x-rays to produce a digital image of a "slice" through a three-dimensional object, was of great importance to medical diagnostics. As well as origination of digital images, digitization of analog images allowed the enhancement and restoration of archaeological artifacts and began to be used in fields as diverse as nuclear medicine, astronomy, law enforcement, defence and industry. Advances in microprocessor technology paved the way for the development and marketing of charge-coupled devices (CCDs) for use in a wide range of image capture devices and gradually displaced the use of analog film and tape in photography and videography towards the end of the 20th century. The computing power necessary to process digital image capture also allowed computer-generated digital images to achieve a level of refinement close to photorealism. === Digital image sensors === The first semiconductor image sensor was the CCD, developed by Willard S. Boyle and George E. Smith at Bell Labs in 1969. While researching MOS technology, they realized that an electric charge was the analogy of the magnetic bubble and that it could be stored on a tiny MOS capacitor. As it was fairly straightforward to fabricate a series of MOS capacitors in a row, they connected a suitable voltage to them so that the charge could be stepped along from one to the next. The CCD is a semiconductor circuit that was later used in the first digital video cameras for television broadcasting. Early CCD sensors suffered from shutter lag. This was largely resolved with the invention of the pinned photodiode (PPD). It was invented by Nobukazu Teranishi, Hiromitsu Shiraki and Yasuo Ishihara at NEC in 1980. It was a photodetector structure with low lag, low noise, high quantum efficiency and low dark current. In 1987, the PPD began to be incorporated into most CCD devices, becoming a fixture in consumer electronic video cameras and then digital still cameras. Since then, the PPD has been used in nearly all CCD sensors and then CMOS sensors. The NMOS active-pixel sensor (APS) was invented by Olympus in Japan during the mid-1980s. This was enabled by advances in MOS semiconductor device fabrication, with MOSFET scaling reaching smaller micron and then sub-micron levels. The NMOS APS was fabricated by Tsutomu Nakamura's team at Olympus in 1985. The CMOS active-pixel sensor (CMOS sensor) was later developed by Eric Fossum's team at the NASA Jet Propulsion Laboratory in 1993. By 2007, sales of CMOS sensors had surpassed CCD sensors. === Digital image compression === An important development in digital image compression technology was the discrete cosine transform (DCT), a lossy compression technique first proposed by Nasir Ahmed in 1972. DCT compression is used in JPEG, which was introduced by the Joint Photographic Experts Group in 1992. JPEG compresses images down to much smaller file sizes, and has become the most widely used image file format on the Internet. == Mosaic == In digital imaging, a mosaic is a combination of non-overlapping images, arranged in some tessellation. Gigapixel images are an example of such digital image mosaics. Satellite imagery are often mosaicked to cover Earth regions. Interactive viewing is provided by virtual-reality photography.

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  • Open Data Center Alliance

    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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