Key & See

Key & See is a variation of the TV Key service that forms part of the open, standards-based interactive TV services platform provided by Miniweb Interactive. Key & See allows viewers to access the interactive TV content made available by broadcasters and channel owners while leaving quarter of their screen tuned to the programme they are already watching Like TV Key, Key & See can be used with interactive TV services on UK satellite TV provider Sky Digital (BSkyB) Key & See works in the same way as a TV Key but the numeric shortcut code is associated with a broadcaster and a particular TV channel or programme. Miniweb Interactive offers commercial brands and broadcasters the chance to utilise TV Key and Key & See technology as part of its interactive TV services platform

Google Vids

Google Vids (not to be confused with Google Video) is an online timeline-based video editing application included as part of the Google Workspace suite. It is designed to help users create informational videos for work-related purposes. The app uses Google's Gemini technology to enable users to create video storyboards manually or with AI assistance using simple prompts. Features include uploading media, choosing stock videos, images, background music, and a voiceover feature with script generation using AI. The app is currently in testing with select Google Workspace Labs users. Like Kapwing and Capcut, Google Vids is primarily for creating work-related content like sales training, onboarding videos, vendor outreach, and project updates. It offers various styles and templates, collaborative features, and is not limited to videos without the short integration at the moment. Google Vids was announced on April 9, 2024. In September 2025, Google began to roll out a basic version of the application to Google Workspace users.

Web data integration

Web data integration (WDI) is the process of aggregating and managing data from different websites into a single, homogeneous workflow. This process includes data access, transformation, mapping, quality assurance and fusion of data. Data that is sourced and structured from websites is referred to as "web data". WDI is an extension and specialization of data integration that views the web as a collection of heterogeneous databases. Data integration techniques in the context of the web, forms the foundation for businesses taking advantage of data available on the ever-increasing number of publicly-accessible websites. Corporate spending on this area amounted to about USD 2.5bn in 2017, and it is expected that by 2020 the market will reach almost USD 7bn.

Bartels–Stewart algorithm

In numerical linear algebra, the Bartels–Stewart algorithm is used to numerically solve the Sylvester matrix equation A X − X B = C {\displaystyle AX-XB=C} . Developed by R.H. Bartels and G.W. Stewart in 1971, it was the first numerically stable method that could be systematically applied to solve such equations. The algorithm works by using the real Schur decompositions of A {\displaystyle A} and B {\displaystyle B} to transform A X − X B = C {\displaystyle AX-XB=C} into a triangular system that can then be solved using forward or backward substitution. In 1979, G. Golub, C. Van Loan and S. Nash introduced an improved version of the algorithm, known as the Hessenberg–Schur algorithm. It remains a standard approach for solving Sylvester equations when X {\displaystyle X} is of small to moderate size. == The algorithm == Let X , C ∈ R m × n {\displaystyle X,C\in \mathbb {R} ^{m\times n}} , and assume that the eigenvalues of A {\displaystyle A} are distinct from the eigenvalues of B {\displaystyle B} . Then, the matrix equation A X − X B = C {\displaystyle AX-XB=C} has a unique solution. The Bartels–Stewart algorithm computes X {\displaystyle X} by applying the following steps: 1.Compute the real Schur decompositions R = U T A U , {\displaystyle R=U^{T}AU,} S = V T B T V . {\displaystyle S=V^{T}B^{T}V.} The matrices R {\displaystyle R} and S {\displaystyle S} are block-upper triangular matrices, with diagonal blocks of size 1 × 1 {\displaystyle 1\times 1} or 2 × 2 {\displaystyle 2\times 2} . 2. Set F = U T C V . {\displaystyle F=U^{T}CV.} 3. Solve the simplified system R Y − Y S T = F {\displaystyle RY-YS^{T}=F} , where Y = U T X V {\displaystyle Y=U^{T}XV} . This can be done using forward substitution on the blocks. Specifically, if s k − 1 , k = 0 {\displaystyle s_{k-1,k}=0} , then ( R − s k k I ) y k = f k + ∑ j = k + 1 n s k j y j , {\displaystyle (R-s_{kk}I)y_{k}=f_{k}+\sum _{j=k+1}^{n}s_{kj}y_{j},} where y k {\displaystyle y_{k}} is the k {\displaystyle k} th column of Y {\displaystyle Y} . When s k − 1 , k ≠ 0 {\displaystyle s_{k-1,k}\neq 0} , columns [ y k − 1 ∣ y k ] {\displaystyle [y_{k-1}\mid y_{k}]} should be concatenated and solved for simultaneously. 4. Set X = U Y V T . {\displaystyle X=UYV^{T}.} === Computational cost === Using the QR algorithm, the real Schur decompositions in step 1 require approximately 10 ( m 3 + n 3 ) {\displaystyle 10(m^{3}+n^{3})} flops, so that the overall computational cost is 10 ( m 3 + n 3 ) + 2.5 ( m n 2 + n m 2 ) {\displaystyle 10(m^{3}+n^{3})+2.5(mn^{2}+nm^{2})} . === Simplifications and special cases === In the special case where B = − A T {\displaystyle B=-A^{T}} and C {\displaystyle C} is symmetric, the solution X {\displaystyle X} will also be symmetric. This symmetry can be exploited so that Y {\displaystyle Y} is found more efficiently in step 3 of the algorithm. == The Hessenberg–Schur algorithm == The Hessenberg–Schur algorithm replaces the decomposition R = U T A U {\displaystyle R=U^{T}AU} in step 1 with the decomposition H = Q T A Q {\displaystyle H=Q^{T}AQ} , where H {\displaystyle H} is an upper-Hessenberg matrix. This leads to a system of the form H Y − Y S T = F {\displaystyle HY-YS^{T}=F} that can be solved using forward substitution. The advantage of this approach is that H = Q T A Q {\displaystyle H=Q^{T}AQ} can be found using Householder reflections at a cost of ( 5 / 3 ) m 3 {\displaystyle (5/3)m^{3}} flops, compared to the 10 m 3 {\displaystyle 10m^{3}} flops required to compute the real Schur decomposition of A {\displaystyle A} . == Software and implementation == The subroutines required for the Hessenberg-Schur variant of the Bartels–Stewart algorithm are implemented in the SLICOT library. These are used in the MATLAB control system toolbox. == Alternative approaches == For large systems, the O ( m 3 + n 3 ) {\displaystyle {\mathcal {O}}(m^{3}+n^{3})} cost of the Bartels–Stewart algorithm can be prohibitive. When A {\displaystyle A} and B {\displaystyle B} are sparse or structured, so that linear solves and matrix vector multiplies involving them are efficient, iterative algorithms can potentially perform better. These include projection-based methods, which use Krylov subspace iterations, methods based on the alternating direction implicit (ADI) iteration, and hybridizations that involve both projection and ADI. Iterative methods can also be used to directly construct low rank approximations to X {\displaystyle X} when solving A X − X B = C {\displaystyle AX-XB=C} .

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

DoorDash

DoorDash, Inc. is an American company operating online food ordering and food delivery. It trades under the symbol DASH. With a 56% market share, DoorDash is the largest food delivery platform in the United States. It also has a 60% market share in the convenience delivery category. As of December 31, 2020, the platform was used by 450,000 merchants, 20 million consumers, and had over one million delivery couriers. Founded by Tony Xu, Andy Fang, Stanley Tang and Evan Moore, DoorDash made its debut on the Fortune 500 list in 2024, ranking No. 443. DoorDash has been sued for or held legally liable for withholding tips, reducing tip transparency, antitrust price manipulation, listing restaurants without permission, misclassifying workers, withholding sick time, and illegally selling personal data. As of April 2026, DoorDash operates in the United States (including Puerto Rico), Canada, Australia, and New Zealand. Through its subsidiaries Deliveroo and Wolt, the company also operates across Europe, as well as in Azerbaijan, Georgia, Israel, Kazakhstan, Kuwait, and the United Arab Emirates. == History == In January 2013, Stanford University students Tony Xu, Stanley Tang, Andy Fang and Evan Moore launched PaloAltoDelivery.com in Palo Alto, California. In the summer of 2013, it received US$120,000 in seed money from Y Combinator in exchange for a 7% stake. It incorporated as DoorDash in June 2013. DoorDash's first partnership with a fast food burger restaurant chain was in April 2016, when it partnered with CKE Restaurants, parent company of Carl's Jr. and Hardee's, for food delivery. In December 2017, DoorDash announced its partnership with Wendy's for delivery from its restaurants. In December 2018, DoorDash overtook Uber Eats to hold the second position in total US food delivery sales, behind GrubHub. By March 2019, it had exceeded GrubHub in total sales, at 27.6% of the on-demand delivery market. By early 2019, DoorDash was the largest food delivery provider in the U.S., as measured by consumer spending. In October 2019, DoorDash opened its first ghost kitchen, DoorDash Kitchen, in Redwood City, California, with four restaurants operating at the location. By June 2020, DoorDash had raised more than $2.5 billion over several financing rounds from investors including Y Combinator, Charles River Ventures, SV Angel, Khosla Ventures, Sequoia Capital, SoftBank Group, GIC, and Kleiner Perkins. DoorDash announced a partnership with KFC in September 2020, followed by Taco Bell in October 2020. In November 2020, DoorDash announced the opening of its first physical restaurant location, partnering up with Bay Area restaurant Burma Bites to offer delivery and pick-up orders. In December 2020, it became a public company via an initial public offering, raising $3.37 billion. In November 2021, DoorDash acquired Finland's Wolt for €7bn. In August 2022, DoorDash announced it would end its partnership with Walmart in September, ending the companies' cooperation agreement from 2018. In November 2022, DoorDash announced plans to lay off 1,250 corporate employees, or about six percent of its workforce, to rein in expenses. In June 2023, DoorDash announced it would give its drivers the option of earning an hourly minimum wage instead of being paid per delivery. However, drivers are only paid hourly when on an active delivery. In September 2023, the company transferred its stock listing from the New York Stock Exchange to the Nasdaq. On December 18, 2023, DoorDash was added to the Nasdaq-100 index. In March 2025, DoorDash announced a partnership with Klarna, a Buy Now, Pay Later (BNPL) service, letting customers schedule small payments over a set period of time. DoorDash received widespread criticism from this decision, including internet mockery, given concerns about the increase of household debt in America. In 2025, DoorDash acquired the UK-based delivery service Deliveroo for $3.88 billion. The combined company operates in 40 countries and serves 50 million users monthly. In September 2025, DoorDash and Ace Hardware (the largest hardware cooperative) announced their partnership to offer delivery for home use products from over 4,000 Ace locations. == Lawsuits against DoorDash == === 2017 class-action lawsuit for misclassifying workers === In 2017, a class-action lawsuit was filed against DoorDash for allegedly misclassifying delivery drivers in California and Massachusetts as independent contractors. In 2022, a tentative settlement was reached in which DoorDash would pay $100 million total, with $61 million going to over 900,000 drivers, paying out just over $130 per driver, and $28 million for the lawyers. Gizmodo criticized the settlement, noting that the $413 million that DoorDash CEO Tony Xu received the previous year was one of the largest CEO compensation packages of all time. === 2019 data breach lawsuit === On May 4, 2019, DoorDash confirmed 4.9 million customers, delivery workers and merchants had sensitive information stolen via a data breach. Those who joined the platform after April 5, 2018, were unaffected by the breach. A class-action lawsuit for the breach was filed against DoorDash in October 2019. === Withholding of tips and subsequent class-action lawsuits === In July 2019, the company's tipping policy was criticized by The New York Times, and later The Verge and Vox and Gothamist. Drivers receive a guaranteed minimum per order that is paid by DoorDash by default. When a customer added a tip, instead of going directly to the driver, it first went to the company to cover the guaranteed minimum. Drivers then only directly received the part of the tip that exceeded the guaranteed minimum per order. In January 2020, it was reported that DoorDash had lied about skimming tips from its drivers, causing them to earn an average of $1.45 an hour after expenses, and that after the company had allegedly overhauled its tipping system, DoorDash was still manipulating per-delivery payouts at the expense of drivers. A DoorDash customer filed a class action lawsuit against the company for its "materially false and misleading" tipping policy. The case was referred to arbitration in August 2020. Under pressure, the company revised its policy. The company settled a lawsuit with District of Columbia Attorney General Karl Racine for $2.5 million, with funds going to deliverers, the government, and to charity. ==== 2021 driver strike for tip transparency ==== In July 2021, DoorDash drivers went on strike to protest lack of tip transparency and to ask for higher pay. At the time of the strike, and, as of June 2022, DoorDash did not allow drivers to see the full tip amounts prior to accepting a delivery in the app. If customers tip over a set amount for the order total, Doordash hides a portion of the tip until the delivery is complete. The strike occurred after DoorDash rewrote its code to cut off access to Para, a third-party app that drivers had been using to see the full tip amounts. ==== 2025 class-action lawsuit settlement ==== In 2025, DoorDash agreed to pay around $17 million for "misleading both consumers and delivery workers" with tips being docked from drivers' pay instead of directly going to drivers. === 2020 antitrust litigation === In April 2020, in the case of Davitashvili v. GrubHub Inc. DoorDash, Grubhub, Postmates, and Uber Eats were accused of monopolistic power by only listing restaurants on its apps if the restaurant owners signed contracts which include clauses that require prices be the same for dine-in customers as for customers receiving delivery. The plaintiffs stated that this arrangement increases the cost for dine-in customers, as they are required to subsidize the cost of delivery; and that the apps charge "exorbitant" fees, which range from 13% to 40% of revenue, while the average restaurant's profit ranges from 3% to 9% of revenue. The lawsuit seeks treble damages, including for overcharges, since April 14, 2016, for dine-in and delivery customers in the United States at restaurants using the defendants’ delivery apps. Although several preliminary documents in the case have now been filed, a trial date has not yet been set. === Litigation for illegal unauthorized restaurant listing === In May 2021, DoorDash was criticized for unauthorized listings of restaurants who had not given permission to appear on the app. The company was sued by Lona's Lil Eats in St. Louis, with the lawsuit claiming that DoorDash had listed them without permission, then prevented any orders to the restaurant from going through and redirecting customers to other restaurants instead, because Lona's was "too far away," when in reality it had not paid DoorDash a fee for listing. This aspect of DoorDash's business practice is illegal in California. === 2021 lawsuit by the city of Chicago === In August 2021, the city of Chicago sued DoorDash and GrubHub. According to Chicago mayor Lori Lightfoot, the companies broke the law by using "unfair and deceptive t

BRS/Search

BRS/Search is a full-text database and information retrieval system. BRS/Search uses a fully inverted indexing system to store, locate, and retrieve unstructured data. It was the search engine that in 1977 powered Bibliographic Retrieval Services (BRS) commercial operations with 20 databases (including the first national commercial availability of MEDLINE); it has changed ownership several times during its development and is currently sold as Livelink ECM Discovery Server by Open Text Corporation. == Early development == Development on what was to become BRS began as Biomedical Communications Network (BCN) at the State University of New York at Albany (SUNY). BCN, which went online in 1968, provided on-line access to nine databases, including MEDLINE and BIOSIS Previews, to large universities and medical schools primarily in the Northeast of the USA. State funding for the project was withdrawn in 1975, and Bibliographic Retrieval Services (BRS) was formed as a non-profit concern the following year. It was incorporated in May 1976 as a for-profit corporation with Ron Quake as president, Jan Egeland as vice president in charge of marketing and training, and Lloyd Palmer as vice president of systems. == BRS commercial operations == In December 1976, the First BRS User Meeting was held in Syracuse, New York, and by January 1977 BRS started commercial operations with 20 databases (including the first national commercial availability of MEDLINE) and 9 million records, using modified IBM STAIRS (STorage And Information Retrieval System) software, Telenet for telecommunications, and timesharing mainframe computers of Carrier Corporation. In October 1980 BRS was sold by Egeland and Quake to Indian Head, Inc., a subsidiary of the Dutch company Thyssen-Bornemisza Group. == 1989–1993 == In 1989 Robert Maxwell acquired BRS and the BRS/Search software; he announced the planned incorporation of the ORBIT Search Service and BRS Information Technologies and renamed the whole group Maxwell Online, Inc. At that time BRS Information Technologies was serving the medical and academic library marketplace with over 150 databases. Maxwell later bought the publishing company Macmillan and put Maxwell Online under Macmillan. In the same year BRS/LINK (hypertext connection of databases; first application delivering full text) was announced. The initial BRS/LINK application "relates the citation in a bibliographic database to its full-text article in a second database," and "eliminates the need to re-execute a search strategy in the second database in order to find the corresponding full-text article." Initially BRS/LINK supported linking only selected bibliographic databases: MEDLINE, Health Planning and Administration, and MEDLINE References on AIDS to the full-text Comprehensive Core Medical Library. At the time of Robert Maxwell’s death in 1991, Macmillan brought in Andrew Gregory to represent the company during the 2 years that Maxwell’s affairs were being settled and to prepare Maxwell Online to be able to sell the components. Maxwell Online shortly thereafter underwent yet another name change, this time to InfoPro Technologies. == Dataware Technologies ownership of BRS/SEARCH == Early in 1994, InfoPro Technologies, a subsidiary of MHC Inc. (holding company for Macmillan Inc.), the former Maxwell Online service, sold off all its subsidiaries. ORBIT Search Services went to the French-owned Questel, the dial-up BRS Search Services to CD Plus Technologies (later to become OVID), and BRS Software Products (including BRS/SEARCH) to Dataware Technologies. Almost up to the end of InfoPro Technologies, BRS Software had been the fastest growing segment of the company. At the 14th BRS North American Users Group Conference in 1999, Dave Schubmehl of Dataware Technologies presented a paper in which he stated "The purpose of this presentation is to update BRS users on upcoming releases of BRS/Search, NetAnswer, and other Dataware products. BRS/Search 7.0 will include features specifically requested by customers, as well as other enhancements. Earlier this year, Dataware acquired Sovereign Hill Software, makers of InQuery. In light of that acquisition, and Dataware's other development projects, we'll look at Dataware's plans for all products, including BRS/Search and NetAnswer." == Open Text acquisition of BRS/Search == In 2001 BRS/Search was acquired by Open Text and became LiveLink ECM Discovery Server. It is now referred to as Open Text Discovery Server. Open Text still supports both BRS/Search and NetAnswer. The core BRS/Search technology in the Open Text portfolio was augmented with other capabilities through various acquisitions. For example, Dataware's acquisition of Sovereign-Hill brought InQuery, “a probabilistic information retrieval system using an inference network”, which was developed by the University of Massachusetts Amherst Center for Intelligent Information Retrieval] out of the UMass CIIR and into the marketplace. A product re-branding table shows the range of products, their old names and their new names. InQuery is a concept search engine that uses noun phrases, parts of speech and other co-occurrence relationships in overlapping passages of text rather than single term inverted indexes of single words in documents. Open Text's portfolio has grown to include Hummingbird Content Management, and has always included BASIS. == 2003 == BRS/Search North America User's Group (BRSNAUG) website with a June 8, 2003 date listed the following features for BRS/Search. The BRSNAUG also disincorporated in 2003. Cross-references to BRS/Search on the World Wide Web point to Open Text Livelink. Engine features include: Rapid query response time. Numerical data handling and elementary statistical processing (sum, avg, min, max) Search results weighting and relevancy ranking Left- and right-truncation and expansion of search terms Superior data compression – loaded databases typically use only about 1.5 times the input stream size in disk space Large capacity databases – up to 100 million documents, each with up to 65,000 paragraphs Fine control of indexing and searching – right down to the word, sentence, and paragraph level Fine control over data security. Document access can be controlled at the database, document, and paragraph level International language support for all 7/8 bit characters sets and customizable language tables Flexible and customizable stop word lists ANSI-compatible thesauri Hypertext links within and between documents and databases (R6.x) Support for natural language parsing of queries Automatic document summarization tools Client/Server development Programming interfaces for World-Wide Web (HTTP, HTML) access to databases