AI Chat Character Apps

AI Chat Character Apps — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • AsoSoft text corpus

    AsoSoft text corpus

    The AsoSoft text corpus is the first large-scale Kurdish text corpus, collected and processed by the AsoSoft research and development group. It contains 458,000 documents (188 million tokens) that are collected from sources such as websites, news agencies, books, and magazines. The corpus is partially tagged by topic, so it can be used for topic identification tasks. Also, it is applicable for extracting language model and computational lexicon information. Part of the corpus (75 million tokens) is available online for non-commercial use. The corpus uses the TEI format.

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  • SQL programming tool

    SQL programming tool

    In the field of software, SQL programming tools provide platforms for database administrators (DBAs) and application developers to perform daily tasks efficiently and accurately. Database administrators and application developers often face constantly changing environments which they rarely completely control. Many changes result from new development projects or from modifications to existing code, which, when deployed to production, do not always produce the expected result. For organizations to better manage development projects and the teams that develop code, suppliers of SQL programming tools normally provide more than facility to the database administrator or application developer to aid in database management and in quality code-deployment practices. == Features == SQL programming tools may include the following features: === SQL editing === SQL editors allow users to edit and execute SQL statements. They may support the following features: cut, copy, paste, undo, redo, find (and replace), bookmarks block indent, print, save file, uppercase/lowercase keyword highlighting auto-completion access to frequently used files output of query result editing query-results committing and rolling-back transactions inside cut paper === Object browsing === Tools may display information about database objects relevant to developers or to database administrators. Users may: view object descriptions view object definitions (DDL) create database objects enable and disable triggers and constraints recompile valid or invalid objects query or edit tables and views Some tools also provide features to display dependencies among objects, and allow users to expand these dependent objects recursively (for example: packages may reference views, views generally reference tables, super/subtypes, and so on). === Session browsing === Database administrators and application developers can use session browsing tools to view the current activities of each user in the database. They can check the resource-usage of individual users, statistics information, locked objects and the current running SQL of each individual session. === User-security management === DBAs can create, edit, delete, disable or enable user-accounts in the database using security-management tools. DBAs can also assign roles, system privileges, object privileges, and storage-quotas to users. === Debugging === Some tools offer features for the debugging of stored procedures: step in, step over, step out, run until exception, breakpoints, view & set variables, view call stack, and so on. Users can debug any program-unit without making any modification to it, including triggers and object types. === Performance monitoring === Monitoring tools may show the database resources — usage summary, service time summary, recent activities, top sessions, session history or top SQL — in easy-to-read graphs. Database administrators can easily monitor the health of various components in the monitoring instance. Application developers may also make use of such tools to diagnose and correct application-performance problems as well as improve SQL server performance. === Test data === Test data generation tools can populate the database by realistic test data for server or client side testing purposes. Also, this kind of software can upload sample blob files to database.

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  • MaPS S.A.

    MaPS S.A.

    MaPS S.A. is a software editor founded in 2011 by Thierry Muller. The company is headquartered in Luxembourg. Its platform, called MaPS System, provides Data Management software for Multichannel Marketing. == History and funding == The first version of MaPS System was released under the agency Prem1um S.A. in 2005 in the partnership with Pingroom agency. In combination with MaPS System, Prem1um also provided various consulting services in Marketing, Publishing and Sales. This is where MaPS System takes its names (M stands for Marketing, P for Publishing and S for Sales). In 2011, after being successful, Prem1um S.A. decided to enable the software MaPS System to operate independently under MaPS S.A., as a separate company and editor of the software. The first financial supports were provided by Malta ICI, a Venture Capital firm, and the local partner Chameleon Invest, a seed-capital fund led by Business Angels, who invested €900,000. In a second investment round in 2014 led by Newion Investments, a Venture Capital firm, €1.4 Million were raised, thus amounting to total assets of €2.2 Million. In 2016, the company was taken over by three private investors. In 2018, after two years of continuous growth and European expansion in Belgium, Germany and Switzerland, MaPS S.A acquired Awevo, an e-commerce web agency. == Products == The services included in MaPS System range from the data centralization, Data Governance to an optimized Multichannel Marketing. The software currently includes more than 35 modules for Master Data Management, Product Information Management, Digital Asset Management, Business Process Management including catalogue Publishing features. == Certifications == In 2019, MaPS System and Awevo received "Made in Luxembourg" label, given to the companies whose services are entirely designed in Luxembourg, without any production or development offshoring. MaPS System is a member of ICT Cluster by Luxinnovation.

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  • Learning augmented algorithm

    Learning augmented algorithm

    A learning augmented algorithm (also called algorithm with predictions) is an algorithm that can make use of a prediction to improve its performance. Whereas in regular algorithms just the problem instance is inputted, learning augmented algorithms accept an extra parameter. This extra parameter often is a prediction of some property of the solution. This prediction is then used by the algorithm to improve its running time or the quality of its output. The most common application are online algorithms, where a prediction on the uncertain instance is provided. == Description == A learning augmented algorithm typically takes an input ( I , A ) {\displaystyle ({\mathcal {I}},{\mathcal {A}})} . Here I {\displaystyle {\mathcal {I}}} is a problem instance and A {\displaystyle {\mathcal {A}}} is the prediction. A prediction can be any object. Common are the following types: Prediction of an optimal solution. The prediction gives a solution to the problem or characterizes an optimal solution. Prediction of the input. This is mainly used for online problems. Prediction of algorithmic actions. A prediction tailored to a specific algorithm that suggests a specific algorithm execution. Learning augmented algorithms usually satisfy the following three properties: Consistency. A learning augmented algorithm is said to be consistent if the algorithm can be proven to have a good performance when it is provided with an accurate prediction. Smoothness. A learning augmented algorithm is called smooth if its performance can be bounded by a function of the quality of the prediction. Here, the quality can be measured in a problem specific way. This is also called the prediction error. Robustness. A learning augmented algorithm is called robust if its worst-case performance can be bounded even if the given prediction is inaccurate. Learning augmented algorithms generally do not prescribe how the prediction should be done. For this purpose machine learning can be used. == Applications == A few examples of problems where learning augmented algorithms have been applied are the following. === Online algorithms === The ski rental problem The weighted paging problem The set cover problem Nonclairvoyant scheduling The online bipartite matching problem === Warm starting === ==== Data structures ==== The binary search algorithm is an algorithm for finding elements of a sorted list x 1 , … , x n {\displaystyle x_{1},\ldots ,x_{n}} . It needs O ( log ⁡ ( n ) ) {\displaystyle O(\log(n))} steps to find an element with some known value y {\displaystyle y} in a list of length n {\displaystyle n} . With a prediction i {\displaystyle i} for the position of y {\displaystyle y} , the following learning augmented algorithm can be used. First, look at position i {\displaystyle i} in the list. If x i = y {\displaystyle x_{i}=y} , the element has been found. If x i < y {\displaystyle x_{i} y {\displaystyle x_{i}>y} , do the same as in the previous case, but instead consider i − 1 , i − 2 , i − 4 , … {\displaystyle i-1,i-2,i-4,\ldots } . The error is defined to be η = | i − i ∗ | {\displaystyle \eta =|i-i^{}|} , where i ∗ {\displaystyle i^{}} is the real index of y {\displaystyle y} . In the learning augmented algorithm, probing the positions i + 1 , i + 2 , i + 4 , … {\displaystyle i+1,i+2,i+4,\ldots } takes log 2 ⁡ ( η ) {\displaystyle \log _{2}(\eta )} steps. Then a binary search is performed on a list of size at most 2 η {\displaystyle 2\eta } , which takes log 2 ⁡ ( η ) {\displaystyle \log _{2}(\eta )} steps. This makes the total running time of the algorithm 2 log 2 ⁡ ( η ) {\displaystyle 2\log _{2}(\eta )} . So, when the error is small, the algorithm is faster than a normal binary search. This shows that the algorithm is consistent. Even in the worst case, the error will be at most n {\displaystyle n} . Then the algorithm takes at most O ( log ⁡ ( n ) ) {\displaystyle O(\log(n))} steps, so the algorithm is robust. ==== More examples ==== The maximum weight matching problem === Approximation algorithms === The maximum cut problem The vertex cover problem === Mechanism Design === The facility location problem

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  • Cumulus (software)

    Cumulus (software)

    Cumulus is a digital asset management software designed for client/server system which is developed by Canto Software. The product makes use of metadata for indexing, organizing, and searching. == History == Cumulus was first released as a Macintosh application in 1992, and was named by Apple Computer as the "Most Innovative Product of 1992". Cumulus introduced search capabilities beyond those available in the Macintosh at the time, particularly relating to thumbnails. Cumulus 1.0 was a single-user product with no network capabilities. Among the main features of Cumulus 1.0, the search function automatically generated previews and contained support for the included AppleTalk – Peer-to-Peer – network. Cumulus 2.5 was available in five different languages and received the 1993 MacUser magazine Eddy award for "Best Publishing & Graphics Utility". In 1995, Canto introduced the scanner software "Cirrus" to focus on the development of Cumulus. Cumulus 3, released in 1996, introduced a server version for the first time and contained the possibility to spread files over the Internet via the "Web Publisher". Since Apple offered Cumulus 3 with its "Workgroup Server" as a bundle, Cumulus became one of the leading digital asset management systems. Cumulus 4 was the first version that was network-ready, and was available for Macintosh, Windows and UNIX operating systems allowing for cross-platform file sharing. Released in 1998, the support of Solaris was discounted later. Cumulus 5 modified the software core to use an open architecture providing an API to external systems and databases. The open architecture of Cumulus 5 also enabled a more functional bridge between Cumulus and the Internet. Cumulus 6 introduced Embedded Java Plugin (EJP) which allowed system integrators to build custom Java plug-ins in order to extend the functionality of the Cumulus client. Cumulus 6.5 marked the end of the Cumulus Single User Edition product, which was licensed to MediaDex for further development and distribution. Cumulus 7 was introduced summer of 2006. Cumulus 8 was released in June 2009, with new indexing capabilities taking advantage of multicore/multiprocessor systems, and ability to manage a wider variety of file formats. Cumulus 8.5 was released in May 2011. Support was added for multilingual metadata, sometimes referred to as "World Metadata." Cumulus Sites was updated to support metadata editing and file uploads. Cumulus 8.6 was released in July 2012, and contains an updated user interface for the administration of Cumulus Sites and additional features for web-based administration of Cumulus. Other additions include features for collaboration links, multi-language support and automated version control. Cumulus 9 was released in September 2013 and introduced a new Web Client User Interface and the Cumulus Video Cloud. The Cumulus Web Client UI was redesigned to provide users with a modern, easy-to-use interface to support and guide the user while addressing modern business needs. The Cumulus Video Cloud extends the Cumulus video handling capabilities to add conversion and global streaming. Cumulus 9 also saw the addition of upload collection links which allow external collaborators to drag and drop files directly into Cumulus without needing a Cumulus account. Cumulus 9.1 was released in May 2014 and introduced the Adobe Drive Adapter for Cumulus which allows users to browse and search digital assets in Cumulus directly from Adobe work environments such as Photoshop, InDesign, Illustrator, Premier and other Adobe applications. Cumulus 10 (Cumulus X) was released July 2015 and introduced two mobile-friendly products: the Cumulus app and Portals. The Cumulus app on iOS was designed to allow users to collaborate either on an iPhone or iPad. Portals is the read-only version of the Cumulus Web Client where users can work with assets that admins allow. Cumulus 10.1 was introduced in January 2016 and included the InDesign Client integration where users can work with Adobe InDesign while accessing their assets from Cumulus. Cumulus 10.2 was introduced in September 2016 and brought the Media Delivery Cloud using Amazon Web Services (AWS). It allows users to manage their media rendition in a single source and distribute media files globally across different channels and devices. Cumulus 10.2.3 was released in February 2017 and came with a "crop and customize photos" feature for Portals and the Web Client. == Product overview == The cataloging of the file via upload into the archive is where Cumulus transfers maximum information about the file from the metadata. For image or photo files, this is typically Exif and IPTC data. The metadata is mainly used to search the archive. The use of embargo data supports license management for copyrighted material. The managed files can be cataloged and their usage can be set. The indexing is based on a predefined taxonomy, which is governed by the internal rules of the organization or by industry standards. You can specify whether files can only be used for specific purposes or only by certain groups of people. The production management system includes version management for files. Via the publication function, the files can be distributed directly via links or e-mails. It's also possible to access from the outside via the Cumulus Portals web interface, which allows a read access to released content from the catalog. There are different variants, starting with the "Workgroup archive server" up to the "Enterprise Business Server" for large companies. Both server and client are extensible through a Java-based plug-in architecture. Since version 7.0, there is a web application based on Ajax with a separate user interface. For access to the Cumulus catalog on mobile, there has been an application for Apple devices based on iOS since 2010. == Miscellaneous == In 2015, Cumulus developer Canto established the first Canto digital asset management (DAM) event. The event is held annually in Berlin. The Henry Stewart team has been hosting DAM conferences since 2006.

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  • Species distribution modelling

    Species distribution modelling

    Species distribution modelling (SDM), also known as environmental (or ecological) niche modelling (ENM), habitat suitability modelling, predictive habitat distribution modelling, and range mapping uses ecological models to predict the distribution of a species across geographic space and time using environmental data. The environmental data are most often climate data (e.g. temperature, precipitation), but can include other variables such as soil type, water depth, and land cover. SDMs are used in several research areas in conservation biology, ecology and evolution. These models can be used to understand how environmental conditions influence the occurrence or abundance of a species, and for predictive purposes (ecological forecasting). Predictions from an SDM may be of a species' future distribution under climate change, a species' past distribution in order to assess evolutionary relationships, or the potential future distribution of an invasive species. Predictions of current and/or future habitat suitability can be useful for management applications (e.g. reintroduction or translocation of vulnerable species, reserve placement in anticipation of climate change). There are two main types of SDMs. Correlative SDMs, also known as climate envelope models, bioclimatic models, or resource selection function models, model the observed distribution of a species as a function of environmental conditions. Mechanistic SDMs, also known as process-based models or biophysical models, use independently derived information about a species' physiology to develop a model of the environmental conditions under which the species can exist. The extent to which such modelled data reflect real-world species distributions will depend on a number of factors, including the nature, complexity, and accuracy of the models used and the quality of the available environmental data layers; the availability of sufficient and reliable species distribution data as model input; and the influence of various factors such as barriers to dispersal, geologic history, or biotic interactions, that increase the difference between the realized niche and the fundamental niche. Environmental niche modelling may be considered a part of the discipline of biodiversity informatics. == History == A. F. W. Schimper used geographical and environmental factors to explain plant distributions in his 1898 Pflanzengeographie auf physiologischer Grundlage (Plant Geography Upon a Physiological Basis) and his 1908 work of the same name. Andrew Murray used the environment to explain the distribution of mammals in his 1866 The Geographical Distribution of Mammals. Robert Whittaker's work with plants and Robert MacArthur's work with birds strongly established the role the environment plays in species distributions. Elgene O. Box constructed environmental envelope models to predict the range of tree species. His computer simulations were among the earliest uses of species distribution modelling. The adoption of more sophisticated generalised linear models (GLMs) made it possible to create more sophisticated and realistic species distribution models. The expansion of remote sensing and the development of GIS-based environmental modelling increase the amount of environmental information available for model-building and made it easier to use. == Correlative vs mechanistic models == === Correlative SDMs === SDMs originated as correlative models. Correlative SDMs model the observed distribution of a species as a function of geographically referenced climatic predictor variables using multiple regression approaches. Given a set of geographically referred observed presences of a species and a set of climate maps, a model defines the most likely environmental ranges within which a species lives. Correlative SDMs assume that species are at equilibrium with their environment and that the relevant environmental variables have been adequately sampled. The models allow for interpolation between a limited number of species occurrences. For these models to be effective, it is required to gather observations not only of species presences, but also of absences, that is, where the species does not live. Records of species absences are typically not as common as records of presences, thus often "random background" or "pseudo-absence" data are used to fit these models. If there are incomplete records of species occurrences, pseudo-absences can introduce bias. Since correlative SDMs are models of a species' observed distribution, they are models of the realized niche (the environments where a species is found), as opposed to the fundamental niche (the environments where a species can be found, or where the abiotic environment is appropriate for the survival). For a given species, the realized and fundamental niches might be the same, but if a species is geographically confined due to dispersal limitation or species interactions, the realized niche will be smaller than the fundamental niche. Correlative SDMs are easier and faster to implement than mechanistic SDMs, and can make ready use of available data. Since they are correlative however, they do not provide much information about causal mechanisms and are not good for extrapolation. They will also be inaccurate if the observed species range is not at equilibrium (e.g. if a species has been recently introduced and is actively expanding its range). In standard SDMs, the distribution of a single species is often modeled, with unique parameters describing how environmental (abiotic) factors influence its occurrence probability. This allows for differentiated responses to environmental drivers among species, but can be problematic for data-deficient species. In contrast, similarities in environmental responses can be accounted for in multi-species SDMs, which model several species jointly using shared or hierarchically related parameters. However, neither approach explicitly accounts for community-level biotic interactions, which can be important in explaining species diversity patterns. Joint species distribution models (joint SDMs or J-SDMs) address this by modeling species co-occurrence patterns directly. The occurrence probability of a given species is thus influenced not only by abiotic drivers but also by inferred biotic associations with other species. This can improve accuracy for rarer taxa and provide insights into community ecology. Both standard SDMs and J-SDMs can be used to generate community-level metrics, such as species richness, by aggregating outputs across multiple species. These can be important for decision-making such as conservation planning. === Mechanistic SDMs === Mechanistic SDMs are more recently developed. In contrast to correlative models, mechanistic SDMs use physiological information about a species (taken from controlled field or laboratory studies) to determine the range of environmental conditions within which the species can persist. These models aim to directly characterize the fundamental niche, and to project it onto the landscape. A simple model may simply identify threshold values outside of which a species can't survive. A more complex model may consist of several sub-models, e.g. micro-climate conditions given macro-climate conditions, body temperature given micro-climate conditions, fitness or other biological rates (e.g. survival, fecundity) given body temperature (thermal performance curves), resource or energy requirements, and population dynamics. Geographically referenced environmental data are used as model inputs. Because the species distribution predictions are independent of the species' known range, these models are especially useful for species whose range is actively shifting and not at equilibrium, such as invasive species. Mechanistic SDMs incorporate causal mechanisms and are better for extrapolation and non-equilibrium situations. However, they are more labor-intensive to create than correlational models and require the collection and validation of a lot of physiological data, which may not be readily available. The models require many assumptions and parameter estimates, and they can become very complicated. Dispersal, biotic interactions, and evolutionary processes present challenges, as they aren't usually incorporated into either correlative or mechanistic models. Correlational and mechanistic models can be used in combination to gain additional insights. For example, a mechanistic model could be used to identify areas that are clearly outside the species' fundamental niche, and these areas can be marked as absences or excluded from analysis. See for a comparison between mechanistic and correlative models. == Niche models (correlative) == There are a variety of mathematical methods that can be used for fitting, selecting, and evaluating correlative SDMs. Models include "profile" methods, which are simple statistical techniques that use e.g. environmental distance to known sites of occurrence such as

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  • Documentation science

    Documentation science

    Documentation science is the study of the recording and retrieval of information. It includes methods for storing, retrieving, and sharing of information captured on physical as well as digital documents. This field is closely linked to the fields of library science and information science but has its own theories and practices. The term documentation science was coined by Belgian lawyer and peace activist Paul Otlet. He is considered to be the forefather of information science. He along with Henri La Fontaine laid the foundations of documentation science as a field of study. Professionals in this field are called documentalists. Over the years, documentation science has grown to become a large and important field of study. Evolving from traditional practices like archiving and retrieval to modern theories about the nature of documents, novel methods for organizing digital information, and applications in libraries, research, healthcare, business, and technology and more. This field continues to evolve in the digital age. == Developments in documentation science == 1895: The International Institute of Bibliography (originally Institut International de Bibliographie, IIB) was established on 12 September 1895, in Brussels, Belgium by Paul Otlet and Henri La Fontaine. It aimed to catalog all recorded knowledge using a universal classification system now known as the Universal Decimal Classification (UDC). 1931: International Institute of Bibliography (originally Institut International de Bibliographie, IIB) was renamed The International Institute for Documentation, (Institut International de Documentation, IID). 1934: Paul Otlet envisioned a “radiated library,” a global network of interconnected documents accessible from anywhere via telecommunication. This early idea is now seen as a forerunner of the internet. 1937: American Documentation Institute was founded (1968 nameshift to American Society for Information Science). 1951: Suzanne Briet published Qu'est-ce que la documentation? where she proposed that “a document is evidence in support of a fact,” expanding the definition to include objects such as animals in zoos when they are part of a scientific study. This was a significant theoretical shift in defining documents. 1965-1990: Documentation departments were established, for example, large research libraries, online computer retrieval systems and more. The persons doing the searches were called documentalists. But with the appearance of first CD-ROM databases in the mid-1980s and later the internet in 1990s, these intermediary searches decreased and most such departments closed or merged with other departments. 1996: "Dokvit", Documentation Studies, was established in 1996 at the University of Tromsø in Norway. 2001: The Document Academy was established. It is an international network that celebrates documentation. It was conducted by The Program of Documentation Studies, University of Tromsø, Norway and The School of Information Management and Systems, UC Berkeley. 2003: The first Document Research Conference (DOCAM), a series of conferences made by the Document Academy. DOCAM '03 (2003) was held 13–15 August 2003 at The School of Information Management and Systems (SIMS) at the University of California, Berkeley. 2007: Michael Buckland, Ronald Day, and Birger Hjørland expanded the theoretical foundations of documentation science. They researched and explored documents to be social artifacts, the role of ideology in classification, and how documents influenced knowledge systems. 2010s: The concept of post-documentation or “documentality” began in the 2010s, which focused on how digital traces (e.g., tweets, logs) function as documents without traditional physical form. This led to new thinking in document theory. 2016–present: The Document Academy's DOCAM conferences have continued, offering ongoing developments in the theory and practice of documentation. Themes include affect, memory, activism, and born-digital records. 2017: The journal Information Research published special issues addressing “document theory,” including views on documentation in virtual environments and digital archives. 2020–present: The growth of research data management (RDM) and open science has made documentation practices central to data sharing, metadata standards, and reproducibility in scientific work. == Theoretical foundations == Documentation science has some deep theories that explain what a document is, how people use documents, and how they are organized. These concepts were introduced by scholars who have not only studied libraries, but also philosophy, language, and social sciences. Suzanne Briet described a document as “any material form of evidence” that is made to be used as proof or to share information. An antelope in a zoo, for example, can be a document because it is being studied, classified, and described. Documents are not just things or materials but are also shaped by society. Michael Buckland noted that documents have meaning only when people agree they are useful or valid as information. He explained a document becomes a document when someone decides to use it as evidence. Ronald Day wrote about how documentation is not neutral, it can be influenced by power, ideology, and politics. He claimed that classification systems, like how libraries organize books, are not just technical tools. They also show what kinds of knowledge are seen as more important than others. In recent years, new theories have been introduced, like “documentality” by Maurizio Ferraris. He proposed that a document does not have to be a paper or file, it can also be something digital like a tweet, a database entry, or a log file, as long as it leaves a trace that can be looked at later. This theory helps explain modern digital documents. == Methodologies and practice == Documentation science includes many methods that help people collect, organize, store, and find information. These practices are used in libraries, archives, research labs, companies, and now also in online systems. === Collecting and creating documents === In the past, documentation work included gathering books, articles, reports, and other printed materials. People created records of these materials manually, using catalog cards, indexes, or bibliographies. Paul Otlet’s work with the Universal Bibliographic Repertory is one example. He created millions of card entries to organize knowledge from around the world. Today, documents are not only created by humans. Computers and machines also generate documents, like log files, metadata, and sensor data. These need new tools and methods for collection and management. === Organizing information === Organizing documents has always been a foundational element of documentation science. Methods like classification (dividing things into groups) and indexing (making lists of topics or keywords) help individuals find what they need. A widely used system is the Universal Decimal Classification (UDC) developed by Otlet and La Fontaine. Another is the Library of Congress Classification (LCC) used in the majority of U.S. libraries. Indexing can be performed by humans or by software programs that read the text and add tags to documents. Metadata is also used to describe documents. Metadata is “data about data” like the title, author, date, and subject of a document. Standards like Dublin Core are used in digital libraries to keep metadata consistent. === Retrieval and access === One of the main objectives of documentation is helping users find the right document. This is called information retrieval. In the past, this meant using catalog drawers or printed indexes. Today, people use search engines, databases, and digital libraries. Modern retrieval tools use Boolean logic, ranking algorithms, and sometimes machine learning to show the most useful results first. This is part of what is studied in both documentation science and information retrieval. === Preservation and archiving === Documents require long-term storage. This is called preservation of documents. Printed documents can be damaged by light, pests, or even time on the other hand digital documents can be deemed worthless if formats become outdated or storage facilities fail. Archivists use methods like migration, which includes moving files to new formats, and emulation, which replicates obsolete systems, to preserve materials. These methods and tools are ever changing as new technologies develop. But the main objective of documentation has remained the same, which is to keep information safe, organized, and easy to find. == Documentation in the digital age == With the expansion of the internet, computers, and cloud storage, documents are no longer just books, papers, or reports. They can now be emails, tweets, videos, websites, databases, or even log files created by machines. === Born-digital documents === Many documents today are created directly in digital form. These are called born-digit

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  • Mobile content management system

    Mobile content management system

    A mobile content management system (MCMs) is a type of content management system (CMS) capable of storing and delivering content and services to mobile devices, such as mobile phones, smart phones, and PDAs. Mobile content management systems may be discrete systems, or may exist as features, modules or add-ons of larger content management systems capable of multi-channel content delivery. Mobile content delivery has unique, specific constraints including widely variable device capacities, small screen size, limitations on wireless bandwidth, sometimes small storage capacity, and (for some devices) comparatively weak device processors. Demand for mobile content management increased as mobile devices became increasingly ubiquitous and sophisticated. MCMS technology initially focused on the business to consumer (B2C) mobile market place with ringtones, games, text-messaging, news, and other related content. Since, mobile content management systems have also taken root in business-to-business (B2B) and business-to-employee (B2E) situations, allowing companies to provide more timely information and functionality to business partners and mobile workforces in an increasingly efficient manner. A 2008 estimate put global revenue for mobile content management at US$8 billion. == Key features == === Multi-channel content delivery === Multi-channel content delivery capabilities allow users not to manage a central content repository while simultaneously delivering that content to mobile devices such as mobile phones, smartphones, tablets and other mobile devices. Content can be stored in a raw format (such as Microsoft Word, Excel, PowerPoint, PDF, Text, HTML etc.) to which device-specific presentation styles can be applied. === Content access control === Access control includes authorization, authentication, access approval to each content. In many cases the access control also includes download control, wipe-out for specific user, time specific access. For the authentication, MCM shall have basic authentication which has user ID and password. For higher security many MCM supports IP authentication and mobile device authentication. === Specialized templating system === While traditional web content management systems handle templates for only a handful of web browsers, mobile CMS templates must be adapted to the very wide range of target devices with different capacities and limitations. There are two approaches to adapting templates: multi-client and multi-site. The multi-client approach makes it possible to see all versions of a site at the same domain (e.g. sitename.com), and templates are presented based on the device client used for viewing. The multi-site approach displays the mobile site on a targeted sub-domain (e.g. mobile.sitename.com). === Location-based content delivery === Location-based content delivery provides targeted content, such as information, advertisements, maps, directions, and news, to mobile devices based on current physical location. Currently, GPS (global positioning system) navigation systems offer the most popular location-based services. Navigation systems are specialized systems, but incorporating mobile phone functionality makes greater exploitation of location-aware content delivery possible.

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  • Sunrise Calendar

    Sunrise Calendar

    Sunrise is a discontinued electronic calendar application for mobile and desktop. The service was launched in 2013 by designers Pierre Valade and Jeremy Le Van. In October 2015, Microsoft announced that they had merged the Sunrise Calendar team into the larger Microsoft Outlook team where they will work closely with the Microsoft Outlook Mobile service. == History == The first iteration of Sunrise launched in 2012 and was a daily email digest of appointments, events and birthdays. Sunrise was launched initially as an iPhone application on February 19, 2013. In June 2013, Sunrise raised $2.2 million (~$2.91 million in 2024) in venture funding from Resolute.vc, NextView Ventures, Lerer Hippeau Ventures, SV Angel, and other angel investment firms like Loïc Le Meur, Dave Morin, Fabrice Grinda. In May 2014, Sunrise launched on Android as well as on the web via a web application. In July 2014, Sunrise announced it had raised $6 million (~$7.81 million in 2024) Series A from Balderton Capital. Bernard Liautaud joined the board. On February 11, 2015, Sunrise Atelier, Inc. was acquired by Microsoft for US$100 million (~$129 million in 2024). On October 28, 2015, Microsoft announced that Sunrise would be discontinued, and its functionality merged into Outlook Mobile. Microsoft later stated that the app would permanently cease functioning on August 31, 2016, but the shutdown was delayed to September 13, 2016, to coincide with an update to Outlook Mobile that incorporates aspects of Sunrise into its calendar interface. == Features == Sunrise allowed users to connect with Google Calendar, iCloud calendar and with Exchange Server. The following third-party services featured integration with Sunrise: Foursquare, GitHub, TripIt, Asana, Evernote, Google Tasks, Trello, Songkick, and Wunderlist. As a web app, users could sign-in and use Sunrise in a web browser, with no downloads required. A native Sunrise app could also be downloaded for OS X 10.9 and later, iOS 8.0 and later (both iPhone and iPad) as well as Android phones and tablets. In May 2015, Sunrise launched Meet, a keyboard for Android and iOS that lets users select available time slots in their calendar to schedule one-to-ones.

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  • Computer and information science

    Computer and information science

    Computer and information science (CIS; also known as information and computer science) is a field that emphasizes both computing and informatics, upholding the strong association between the fields of information sciences and computer sciences and treating computers as a tool rather than a field. Information science is one with a long history, unlike the relatively very young field of computer science, and is primarily concerned with gathering, storing, disseminating, sharing and protecting any and all forms of information. It is a broad field, covering a myriad of different areas but is often referenced alongside computer science because of the incredibly useful nature of computers and computer programs in helping those studying and doing research in the field – particularly in helping to analyse data and in spotting patterns too broad for a human to intuitively perceive. While information science is sometimes confused with information theory, the two have vastly different subject matter. Information theory focuses on one particular mathematical concept of information while information science is focused on all aspects of the processes and techniques of information. Computer science, in contrast, is less focused on information and its different states, but more, in a very broad sense, on the use of computers – both in theory and practice – to design and implement algorithms in order to aid the processing of information during the different states described above. It has strong foundations in the field of mathematics, as the very first recognised practitioners of the field were renowned mathematicians such as Alan Turing. Information science and computing began to converge in the 1950s and 1960s, as information scientists started to realize the many ways computers would improve information storage and retrieval. == Terminology == Due to the distinction between computers and computing, some of the research groups refer to computing or datalogy. The French refer to computer science as the term informatique. The term information and communications technology (ICT), refers to how humans communicate with using machines and computers, making a distinction from information and computer science, which is how computers use and gain information. Informatics is also distinct from computer science, which encompasses the study of logic and low-level computing issues. == Education == Universities may confer degrees with a major in computer and information science, not to be confused with a more specific Bachelor of Computer Science or respective graduate computer science degrees. The QS World University Rankings is one of the most widely recognised and distinguished university comparisons. They ranked the top 10 universities for computer science and information systems in 2015. They are: Massachusetts Institute of Technology (MIT) Stanford University University of Oxford Carnegie Mellon University Harvard University University of California, Berkeley (UCB) University of Cambridge The Hong Kong University of Science and Technology Swiss Federal Institute of Technology (ETH Zurich) Princeton University A Computer Information Science degree gives students both network and computing knowledge which is needed to design, develop, and assist information systems which helps to solve business problems and to support business problems and to support business operations and decision making at a managerial level also. == Areas of information and computer science == Due to the nature of this field, many topics are also shared with computer science and information systems. The discipline of Information and Computer Science spans a vast range of areas from basic computer science theory (algorithms and computational logic) to in depth analysis of data manipulation and use within technology. === Programming theory === The process of taking a given algorithm and encoding it into a language that can be understood and executed by a computer. There are many different types of programming languages and various different types of computers, however, they all have the same goal: to turn algorithms into machine code. Popular programming languages used within the academic study of CIS include, but are not limited to: Java, Python, C#, C++, Perl, Ruby, Pascal, Swift, Visual Basic. === Information and information systems === The academic study of software and hardware systems that process large quantities and data, support large scale data management and how data can be used. This is where the field is unique from the standard study of computer science. The area of information systems focuses on the networks of hardware and software that are required to process, manipulate and distribute such data. === Computer systems and organisations === The process of analysing computer architecture and various logic circuits. This involves looking at low level computer processes at bit level computation. This is an in-depth look into the hardware processing of a computational system, involving looking at the basic structure of a computer and designing such systems. This can also involve evaluating complex circuit diagrams, and being able to construct these to solve a main problem. The main purpose behind this area of study is to achieve an understanding of how computers function on a basic level, often through tracing machine operations. === Machines, languages, and computation === This is the study into fundamental computer algorithms, which are the basis to computer programs. Without algorithms, no computer programs would exist. This also involves the process of looking into various mathematical functions behind computational algorithms, basic theory and functional (low level) programming. In an academic setting, this area would introduce the fundamental mathematical theorems and functions behind theoretical computer science which are the building blocks for other areas in the field. Complex topics such as; proofs, algebraic functions and sets will be introduced during studies of CIS. == Developments == Information and computer science is a field that is rapidly developing with job prospects for students being extremely promising with 75.7% of graduates gaining employment. Also the IT industry employs one in twenty of the workforce with it predicted to increase nearly five times faster than the average of the UK and between 2012 and 2017 more than half a million people will be needed within the industry and the fact that nine out of ten tech firms are suffering from candidate shortages which is having a negative impact on their business as it delays the creation and development of new products, and it's predicted in the US that in the next decade there will be more than one million jobs in the technology sector than computer science graduates to fill them. Because of this programming is now being taught at an earlier age with an aim to interest students from a young age into computer and information science hopefully leading more children to study this at a higher level. For example, children in England will now be exposed to computer programming at the age of 5 due to an updated national curriculum. == Employment == Due to the wide variety of jobs that now involve computer and information science related tasks, it is difficult to provide a comprehensive list of possible jobs in this area, but some of the key areas are artificial intelligence, software engineering and computer networking and communication. Work in this area also tends to require sufficient understanding of mathematics and science. Moreover, jobs that having a CIS degree can lead to, include: systems analyst, network administrator, system architect, information systems developer, web programmer, or software developer. The earning potential for CIS graduates is quite promising. A 2013 survey from the National Association of Colleges and Employers (NACE) found that the average starting salary for graduates who earned a degree in a computer related field was $59,977, up 4.3% from the prior year. This is higher than other popular degrees such as business ($54,234), education ($40,480) and math and sciences ($42,724). Furthermore, Payscale ranked 129 college degrees based on their graduates earning potential with engineering, math, science, and technology fields dominating the ranking. With eight computer related degrees appearing among the top 30. With the lowest starting salary for these jobs being $49,900. A Rasmussen College article describes various jobs CIS graduates may obtain with software applications developers at the top making a median income of $98,260. According to the National Careers Service an Information Scientist can expect to earn £24,000+ per year as a starting salary.

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  • Umbrella review

    Umbrella review

    In medical research, an umbrella review is a review of systematic reviews or meta-analyses. They may also be called overviews of reviews, reviews of reviews, summaries of systematic reviews, or syntheses of reviews. Umbrella reviews are among the highest levels of evidence currently available in medicine. By summarizing information from multiple overview articles, umbrella reviews make it easier to review the evidence and allow for comparison of results between each of the individual reviews. Umbrella reviews may address a broader question than a typical review, such as discussing multiple different treatment comparisons instead of only one. They are especially useful for developing guidelines and clinical practice, and when comparing competing interventions.

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  • NoSQL

    NoSQL

    NoSQL (originally meaning "not only SQL" or "non-relational") refers to a type of database design that stores and retrieves data differently from the traditional table-based structure of relational databases. Unlike relational databases, which organize data into rows and columns like a spreadsheet, NoSQL databases use a single data structure—such as key–value pairs, wide columns, graphs, or documents—to hold information. Since this non-relational design does not require a fixed schema, it scales easily to manage large, often unstructured datasets. NoSQL systems are sometimes called "Not only SQL" because they can support SQL-like query languages or work alongside SQL databases in polyglot-persistent setups, where multiple database types are combined. Non-relational databases date back to the late 1960s, but the term "NoSQL" emerged in the early 2000s, spurred by the needs of Web 2.0 companies like social media platforms. NoSQL databases are popular in big data and real-time web applications due to their simple design, ability to scale across clusters of machines (called horizontal scaling), and precise control over data availability. These structures can speed up certain tasks and are often considered more adaptable than fixed database tables. However, many NoSQL systems prioritize speed and availability over strict consistency (per the CAP theorem), using eventual consistency—where updates reach all nodes eventually, typically within milliseconds, but may cause brief delays in accessing the latest data, known as stale reads. While most lack full ACID transaction support, some, like MongoDB, include it as a key feature. == Barriers to adoption == Barriers to wider NoSQL adoption include their use of low-level query languages instead of SQL, inability to perform ad hoc joins across tables, lack of standardized interfaces, and significant investments already made in relational databases. Some NoSQL systems risk losing data through lost writes or other forms, though features like write-ahead logging—a method to record changes before they’re applied—can help prevent this. For distributed transaction processing across multiple databases, keeping data consistent is a challenge for both NoSQL and relational systems, as relational databases cannot enforce rules linking separate databases, and few systems support both ACID transactions and X/Open XA standards for managing distributed updates. Limitations within the interface environment are overcome using semantic virtualization protocols, such that NoSQL services are accessible to most operating systems. == History == The term NoSQL was used by Carlo Strozzi in 1998 to name his lightweight Strozzi NoSQL open-source relational database that did not expose the standard Structured Query Language (SQL) interface, but was still relational. His NoSQL RDBMS is distinct from the around-2009 general concept of NoSQL databases. Strozzi suggests that, because the current NoSQL movement "departs from the relational model altogether, it should therefore have been called more appropriately 'NoREL'", referring to "not relational". Johan Oskarsson, then a developer at Last.fm, reintroduced the term NoSQL in early 2009 when he organized an event to discuss "open-source distributed, non-relational databases". The name attempted to label the emergence of an increasing number of non-relational, distributed data stores, including open source clones of Google's Bigtable/MapReduce and Amazon's DynamoDB. == Types and examples == There are various ways to classify NoSQL databases, with different categories and subcategories, some of which overlap. What follows is a non-exhaustive classification by data model, with examples: === Key–value store === Key–value (KV) stores use the associative array (also called a map or dictionary) as their fundamental data model. In this model, data is represented as a collection of key–value pairs, such that each possible key appears at most once in the collection. The key–value model is one of the simplest non-trivial data models, and richer data models are often implemented as an extension of it. The key–value model can be extended to a discretely ordered model that maintains keys in lexicographic order. This extension is computationally powerful, in that it can efficiently retrieve selective key ranges. Key–value stores can use consistency models ranging from eventual consistency to serializability. Some databases support ordering of keys. There are various hardware implementations, and some users store data in memory (RAM), while others on solid-state drives (SSD) or rotating disks (aka hard disk drive (HDD)). === Document store === The central concept of a document store is that of a "document". While the details of this definition differ among document-oriented databases, they all assume that documents encapsulate and encode data (or information) in some standard formats or encodings. Encodings in use include XML, YAML, and JSON and binary forms like BSON. Documents are addressed in the database via a unique key that represents that document. Another defining characteristic of a document-oriented database is an API or query language to retrieve documents based on their contents. Different implementations offer different ways of organizing and/or grouping documents: Collections Tags Non-visible metadata Directory hierarchies Compared to relational databases, collections could be considered analogous to tables and documents analogous to records. But they are different – every record in a table has the same sequence of fields, while documents in a collection may have fields that are completely different. === Graph === Graph databases are designed for data whose relations are well represented as a graph consisting of elements connected by a finite number of relations. Examples of data include social relations, public transport links, road maps, network topologies, etc. Graph databases and their query language == Performance == The performance of NoSQL databases is usually evaluated using the metric of throughput, which is measured as operations per second. Performance evaluation must pay attention to the right benchmarks such as production configurations, parameters of the databases, anticipated data volume, and concurrent user workloads. Ben Scofield rated different categories of NoSQL databases as follows: Performance and scalability comparisons are most commonly done using the YCSB benchmark. == Handling relational data == Since most NoSQL databases lack ability for joins in queries, the database schema generally needs to be designed differently. There are three main techniques for handling relational data in a NoSQL database. (See table join and ACID support for NoSQL databases that support joins.) === Multiple queries === Instead of retrieving all the data with one query, it is common to do several queries to get the desired data. NoSQL queries are often faster than traditional SQL queries, so the cost of additional queries may be acceptable. If an excessive number of queries would be necessary, one of the other two approaches is more appropriate. === Caching, replication and non-normalized data === Instead of only storing foreign keys, it is common to store actual foreign values along with the model's data. For example, each blog comment might include the username in addition to a user id, thus providing easy access to the username without requiring another lookup. When a username changes, however, this will now need to be changed in many places in the database. Thus this approach works better when reads are much more common than writes. === Nesting data === With document databases like MongoDB it is common to put more data in a smaller number of collections. For example, in a blogging application, one might choose to store comments within the blog post document, so that with a single retrieval one gets all the comments. Thus in this approach a single document contains all the data needed for a specific task. == ACID and join support == A database is marked as supporting ACID properties (atomicity, consistency, isolation, durability) or join operations if the documentation for the database makes that claim. However, this doesn't necessarily mean that the capability is fully supported in a manner similar to most SQL databases. == Query optimization and indexing in NoSQL databases == Different NoSQL databases, such as DynamoDB, MongoDB, Cassandra, Couchbase, HBase, and Redis, exhibit varying behaviors when querying non-indexed fields. Many perform full-table or collection scans for such queries, applying filtering operations after retrieving data. However, modern NoSQL databases often incorporate advanced features to optimize query performance. For example, MongoDB supports compound indexes and query-optimization strategies, Cassandra offers secondary indexes and materialized views, and Redis employs custom indexing mechanisms tailored to specific use cases. Systems like El

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  • 24SevenOffice

    24SevenOffice

    24SevenOffice is a Norwegian software company headquartered in Oslo, Norway, with offices in Stockholm, Sweden and London, United Kingdom. Founded in 1997, the company specializes in web-based (SaaS) ERP and CRM systems. == Company history == 24SevenOffice was founded in 1997 in Porsgrunn, Norway, as IKT Interactive AS and marketed as kontorplassen.no. The name "24SevenOffice" was introduced for the company's London branch when the company entered the British market in 2003. The company changed its name to 24SevenOffice in February 2005. Originally based in Skien, the company later moved to Oslo Innovation Center, then to Tjuvholmen in the waterfront Fjord City of Oslo, and now the headquarters are located in Inkognitogaten 33, Solli plass, Oslo. The idea for the company's product was developed in 1996, and 24SevenOffice was an early innovator in the Scandinavian market in web-based enterprise resource planning solutions (ERP). A British office was established at Surrey Business Park in May 2003, with the company launching its web-based (SaaS) utility computing system to the UK SME market in 2004. An office in Chennai, India, was established in 2005, and 24SevenOffice entered the Swedish market when they acquired the leading competitor and ERP-provider Start & Run in a cash deal. In August 2005, the company had an initial public offering that raised NOK 15 million, and the company entered The Norwegian Over the Counter Market list as of 5 October 2005 (the ticker was 24SO), reaching a market value of NOK 175 million, with 5000 customers in Norway. In 2006, the company signed a deal to sponsor rally driver Petter Solberg, at the time the largest private sponsorship in Norwegian sport. Instead of receiving NOK 5 million in cash, Solberg received a 2.9 per cent ownership in the company. The company entered the German-speaking market in April 2006 when an office in Frankfurt am Main was opened. In late August/early September, they established an office with ten sales agents plus a general manager in Stockholm for the Swedish market. 24SevenOffice initiated strategic cooperation with Active 24 in early 2006 to develop a common platform. During the summer, Active 24 was bought by 24SevenOffice's ERP/CRM competitor Mamut (company), and 24SevenOffice terminated the contract with Active 24 in October demanding NOK 200 million in compensation for lost revenue. After a breakdown of settlement negotiations in the Forliksråd in January 2007, 24SevenOffice filed a case against Active 24 for breach of agreement in the Oslo District Court in March. 24SevenOffice lost on all counts in the District Court in December 2007. In January 2008, 24SevenOffice appealed the case to the Borgarting Court of Appeal, reducing the cause of action from NOK 250 to 30 million. 24SevenOffice lost on all counts in the Court of Appeal in December 2008, and was ordered to cover the costs incurred by Active 24 in connection with the dispute totaling NOK 6.91 million. 24SevenOffice appealed the case to the Supreme Court of Norway, but the Supreme Court Appeals Committee in March 2008 unanimously rejected the appeal from 24SevenOffice over the Borgarting Appeal Court's unanimous judgment of December 2008. On a counterclaim from Active 24 and Mamut against 24SevenOffice, the Oslo District Court in May 2010 found, that 24SevenOffice should pay Active 24 NOK 12 million in compensation for wrongfully having terminated the agreement, and a further NOK 360.000 of the opponent's legal costs. 24SevenOffice disagreed with the court ruling, and appealed once again. The Borgarting Court of Appeal in November 2011, ruled to reduce the amount of damages to NOK 4.4 million plus NOK 900.000 in penal interest. With several scrip issues, 24SevenOffice raised 25 million NOK (about $4 million at the time) between October 2005 and July 2006. They entered into a strategic partnership with Bluegarden, who for 30 years had delivered digital services for payroll, human resource planning, recruitment and training, in March 2006, and they made a large-scale agreement in April 2006, with US telecommunications software company Webex, a competitor to Norwegian Tandberg videoconferencing equipment manufacturer. In September 2006, 24SevenOffice signed an agreement with Fokus Bank to provide their customers with extended functionality in Internet banking. 24SevenOffice had by 2007 reportedly 9000 customers, joined the OpenAjax Alliance, and entered into a strategic partnership with Dun & Bradstreet in May 2007, but despite getting listed on Oslo Axess on 22 June (ticker: TFSO), reaching a market capitalization of NOK 120 million, the company was still losing money. The company ended 2007 with a revenue of NOK 21.7 million. In 2008, 24SevenOffice bought 50% of the stocks in telecommunication company Oyatel, partnered with Nets Group to facilitate invoicing for businesses, and telecommunications company Telipol chose 24SevenOffice's second-generation Internet platform for its 8,000 users. They announced an increase in revenues in Q2 to 11.1 million, up from 4.7 million in the same period the year before. 24SevenOffice had a turnover of NOK 37 million in the first half of 2009, a doubling compared to the same period the previous year, and presented its first positive EBITDA in Q2. The Norwegian Association of Auditors signed an agreement with 24SevenOffice in 2011, whereby they only recommend 24SevenOffice as a system for their members to use. On 27 June 2013, the shareholders of 24SevenOffice took off from the stock exchange and privatized the company. In recent years, the company has invested heavily in finance and accounting – and got leading auditing companies such as PwC and KPMG on the customer list. == Products == 24SevenOffice is a web-based (SaaS) ERP system. It includes modules for CRM, accounting, invoicing, e-mail, file/document management and project management. == Awards == 24SevenOffice won the Seal of Excellence in Multimedia Award at the 2004 CeBIT, became Norwegian Gazelle Company of the year 2004, chosen by Dagens Næringsliv and Dun & Bradstreet, won Product of the Year in the Norwegian finance magazine Kapital, and the IKT Grenland Innovation Award in 2008.

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  • Concordance (publishing)

    Concordance (publishing)

    A concordance is an alphabetical list of the principal words used in a book or body of work, listing every instance of each word with its immediate context. Historically, concordances have been compiled only for works of special importance, such as the Vedas, Bible, Qur'an or the works of Shakespeare, James Joyce or classical Latin and Greek authors, because of the time, difficulty, and expense involved in creating a concordance in the pre-computer era. A concordance is more than an index, with additional material such as commentary, definitions and topical cross-indexing which makes producing one a labor-intensive process even when assisted by computers. In the precomputing era, search technology was unavailable, and a concordance offered readers of long works such as the Bible something comparable to search results for every word that they would have been likely to search for. Today, the ability to combine the result of queries concerning multiple terms (such as searching for words near other words) has reduced interest in concordance publishing. In addition, mathematical techniques such as latent semantic indexing have been proposed as a means of automatically identifying linguistic information based on word context. A bilingual concordance is a concordance based on aligned parallel text. A topical concordance is a list of subjects that a book covers (usually The Bible), with the immediate context of the coverage of those subjects. Unlike a traditional concordance, the indexed word does not have to appear in the verse. The best-known topical concordance is Nave's Topical Bible. The first Bible concordance was compiled for the Vulgate Bible by Hugh of St Cher (d.1262), who employed 500 friars to assist him. In 1448, Rabbi Mordecai Nathan completed a concordance to the Hebrew Bible. It took him ten years. A concordance to the Greek New Testament was published in 1546 by Sixt Birck, and the Septuagint was done a by Conrad Kircher in 1602. The first concordance to the English Bible was published in 1550 by John Merbecke. According to Cruden, it did not employ the verse numbers devised by Robert Stephens in 1545, but "the pretty large concordance" of Mr Cotton did. Then followed Cruden's Concordance and Strong's Concordance. == Use in linguistics == Concordances are frequently used in linguistics, when studying a text. For example: comparing different usages of the same word analysing keywords analysing word frequencies finding and analysing phrases and idioms finding translations of subsentential elements, e.g. terminology, in bitexts and translation memories creating indexes and word lists (also useful for publishing) Concordancing techniques are widely used in national text corpora such as American National Corpus (ANC), British National Corpus (BNC), and Corpus of Contemporary American English (COCA) available on-line. Stand-alone applications that employ concordancing techniques are known as concordancers or more advanced corpus managers. Some of them have integrated part-of-speech taggers (POS taggers) and enable the user to create their own POS-annotated corpora to conduct various types of searches adopted in corpus linguistics. == Inversion == The reconstruction of the text of some of the Dead Sea Scrolls involved a concordance. Access to some of the scrolls was governed by a "secrecy rule" that allowed only the original International Team or their designates to view the original materials. After the death of Roland de Vaux in 1971, his successors repeatedly refused to even allow the publication of photographs to other scholars. This restriction was circumvented by Martin Abegg in 1991, who used a computer to "invert" a concordance of the missing documents made in the 1950s which had come into the hands of scholars outside of the International Team, to obtain an approximate reconstruction of the original text of 17 of the documents. This was soon followed by the release of the original text of the scrolls.

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  • Microsoft SQL Server Master Data Services

    Microsoft SQL Server Master Data Services

    Microsoft SQL Server Master Data Services (MDS) is a Master Data Management (MDM) product from Microsoft that ships as a part of the Microsoft SQL Server relational database management system. Master data management (MDM) allows an organization to discover and define non-transactional lists of data, and compile maintainable, reliable master lists. Master Data Services first shipped with Microsoft SQL Server 2008 R2. Microsoft SQL Server 2016 introduced enhancements to Master Data Services, such as improved performance and security, and the ability to clear transaction logs, create custom indexes, share entity data between different models, and support for many-to-many relationships. == Overview == In Master Data Services, the model is the highest level container in the structure of your master data. You create a model to manage groups of similar data. A model contains one or more entities, and entities contain members that are the data records. An entity is similar to a table. Like other MDM products, Master Data Services aims to create a centralized data source and keep it synchronized, and thus reduce redundancies, across the applications which process the data. Sharing the architectural core with Stratature +EDM, Master Data Services uses a Microsoft SQL Server database as the physical data store. It is a part of the Master Data Hub, which uses the database to store and manage data entities. It is a database with the software to validate and manage the data, and keep it synchronized with the systems that use the data. The master data hub has to extract the data from the source system, validate, sanitize and shape the data, remove duplicates, and update the hub repositories, as well as synchronize the external sources. The entity schemas, attributes, data hierarchies, validation rules and access control information are specified as metadata to the Master Data Services runtime. Master Data Services does not impose any limitation on the data model. Master Data Services also allows custom Business rules, used for validating and sanitizing the data entering the data hub, to be defined, which is then run against the data matching the specified criteria. All changes made to the data are validated against the rules, and a log of the transaction is stored persistently. Violations are logged separately, and optionally the owner is notified, automatically. All the data entities can be versioned. Master Data Services allows the master data to be categorized by hierarchical relationships, such as employee data are a subtype of organization data. Hierarchies are generated by relating data attributes. Data can be automatically categorized using rules, and the categories are introspected programmatically. Master Data Services can also expose the data as Microsoft SQL Server views, which can be pulled by any SQL-compatible client. It uses a role-based access control system to restrict access to the data. The views are generated dynamically, so they contain the latest data entities in the master hub. It can also push out the data by writing to some external journals. Master Data Services also includes a web-based UI for viewing and managing the data. It uses ASP.NET in the back-end. The Silverlight front-end was replaced with HTML5 in SQL Server 2019. Master Data Services provides a Web service interface to expose the data, as well as an API, which internally uses the exposed web services, exposing the feature set, programmatically, to access and manipulate the data. It also integrates with Active Directory for authentication purposes. Unlike +EDM, Master Data Services supports Unicode characters, as well as support multilingual user interfaces. SQL Server 2016 introduced a significant performance increase in Master Data Services over previous versions. == Terminology == Model is the highest level of an MDS instance. It is the primary container for specific groupings of master data. In many ways it is very similar to the idea of a database. Entities are containers created within a model. Entities provide a home for members, and are in many ways analogous to database tables. (e.g. Customer) Members are analogous to the records in a database table (Entity) e.g. Will Smith. Members are contained within entities. Each member is made up of two or more attributes. Attributes are analogous to the columns within a database table (Entity) e.g. Surname. Attributes exist within entities and help describe members (the records within the table). Name and Code attributes are created by default for each entity and serve to describe and uniquely identify leaf members. Attributes can be related to other attributes from other entities which are called 'domain-based' attributes. This is similar to the concept of a foreign key. Other attributes however, will be of type 'free-form' (most common) or 'file'. Attribute Groups are explicitly defined collections of particular attributes. Say you have an entity "customer" that has 50 attributes — too much information for many of your users. Attribute groups enable the creation of custom sets of hand-picked attributes that are relevant for specific audiences. (e.g. "customer - delivery details" that would include just their name and last known delivery address). This is very similar to a database view. Hierarchies organize members into either Derived or Explicit hierarchical structures. Derived hierarchies, as the name suggests, are derived by the MDS engine based on the relationships that exist between attributes. Explicit hierarchies are created by hand using both leaf and consolidated members. Business Rules can be created and applied against model data to ensure that custom business logic is adhered to. In order to be committed into the system data must pass all business rule validations applied to them. e.g. Within the Customer Entity you may want to create a business rule that ensures all members of the 'Country' Attribute contain either the text "USA" or "Canada". The Business Rule once created and ran will then verify all the data is correct before it accepts it into the approved model. Versions provide system owners / administrators with the ability to Open, Lock or Commit a particular version of a model and the data contained within it at a particular point in time. As the content within a model varies, grows or shrinks over time versions provide a way of managing metadata so that subscribing systems can access to the correct content.

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