AI Data Bias

AI Data Bias — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Morphobank

MorphoBank is a web application for collaborative evolutionary research, specifically phylogenetic systematics or cladistics, on the phenotype. Historically, scientists conducting research on phylogenetic systematics have worked individually or in small groups employing traditional single-user software applications such as MacClade, Mesquite and Nexus Data Editor. As the hypotheses under study have grown more complex, large research teams have assembled to tackle the problem of discovering the Tree of Life for the estimated 4-100 million living species(Wilson 2003, pp. 77–80) and the many thousands more extinct species known from fossils. Because the phenotype is fundamentally visual, and as phenotype-based phylogenetic studies have continued to increase in size, it becomes important that observations be backed up by labeled images. Traditional desktop software applications currently in wide use do not provide robust support for team-based research or for image manipulation and storage. MorphoBank is a particularly important tool for the growing scientific field of phenomics. The development of MorphoBank, which began in 2001, has been funded by the National Science Foundation's Directorates for Geosciences, Biological Sciences and Computer and Information Science and Engineering. The significance of the scientific work on MorphoBank has been featured in the New York Times(here and here), among other publications. == Advantages == Teams of scientists studying phylogenetics to build the Tree of Life assemble large spreadsheets of observations about species (referred to as "matrices"). These teams require simultaneous access by each team member to a single and secure copy of the team's data during a scientific research project. This single copy of the data also changes with great frequency during the data collection phase. Images that can be very helpful for documenting homology statements must be displayed, labeled and shared as homology statements develop. This cannot be accomplished elegantly with a desktop software package alone because in a desktop environment each collaborator is working on his own private copy of project data. Changes made by one participant cannot automatically propagate to others, preventing collaborators from seeing each other's data edits until they are manually (and due to the effort involved, often only periodically) merged into a single "true" dataset. In all but the smallest and most disciplined of teams, file version control and the reconciliation of changes made on multiple copies of the data emerge quickly as significant drags on productivity. MorphoBank is an attempt to address these issues by leveraging the ubiquity of the web and modern web-based application techniques, including Ajax, web service layers, and rich web applications to provide a full-featured, net-accessible collaborative workspace for phylogenetic research. In particular, MorphoBank makes it easy to: Share all kinds of data with geographically separated team members, including taxonomy, character and specimen data, media (including images, video and audio), phylogenetic matrices (including data in the widely used NEXUS and TNT format) and other data such as documents and genetic sequences. Label high-resolution images using a web-based image annotation application. Collaboratively edit project data such as phylogenetic matrices using a built-in web-based matrix editor. The editor allows the linking of labeled images to individual cells of a matrix. Manage access to project data. Access ranges from full-access for team members to anonymous read-only access for potential reviewers. Publish completed project data on the web in support of a published paper with a persistent URL. Search The Encyclopedia of Life for taxon exemplar images. Store high resolution CT data Create ontologies for updating and populating matrix cells. These tasks are difficult or impossible in most existing software applications. == History == In 2001 the National Science Foundation (NSF) sponsored a workshop, at the American Museum of Natural History in New York to develop the outlines of a web-based system for a collaborative, media-rich research tool for morphological phylogenetics. An application prototype presented at the workshop was later refined with feedback from the workshop and became MorphoBank version 1.0. A grant from the US National Oceanic and Atmospheric Administration funded further revisions resulting in version 2.0, released in 2005. Current support from the NSF is funding current feature enhancements to MorphoBank. MorphoBank was hosted by Stony Brook University until late October 2021 and received back up support from the American Museum of Natural History. The current version is 3.0. Rationale for the software was described in the journal Cladistics. MorphoBank has also received support from NESCENT and the San Diego Supercomputer Center. Since 2018, MorphoBank has been supported in part by Phoenix Bioinformatics, a non-profit company founded to sustain databases for the basic sciences. A permanent move of MorphoBank from Stony Brook University to Phoenix Bioinformatics was complete in late October 2021. The San Diego Supercomputer Center has previously provided technical and hosting resources to the MorphoBank project. == Usage == MorphoBank hosts the products of peer-reviewed scientific research on phenotypes. An increasing volume of systematics data is "born digital" and MorphoBank is well suited to handle this type of material. On August 24, 2007, 62 active research projects were hosted by MorphoBank, as well as 6 completed (and published) projects. By 2017 over 2000 scientists and their students were registered content builders (users are not required to register and are even more numerous) and has more than 500 publicly available projects with approximately 80,000 images that are the products of scientific research. Over 1,500 active research projects are hosted by MorphoBank. The software has been used to assemble phylogenetic research on such groups as mammals, from bats to whales, bivalve molluscs, arachnids, fossil plants and living and extinct amniotes. It has also been used more broadly in evolutionary and paleontological research to host curated images associated with published research on lacewing insects geckos, raptor birds, dinosaurs, frogs and nematodes. MorphoBank is increasingly used in conjunction with the Paleobiology Database. Example published projects: Project 1097: Blank CE, 2013 Origin and early evolution of photosynthetic eukaryotes in freshwater environments – reinterpreting proterozoic paleobiology and biogeochemical processes in light of trait evolution Project 2520: Carvalho, T. P., R. E. Reis, and J. P. Friel, 2017 A new species of Hoplomyzon (Siluriformes: Aspredinidae) from Maracaibo Basin, Venezuela: osteological description using high-resolution Project 2651: Baron, M. G., Norman, D. B., Barrett, P. M., 2017 A new hypothesis of dinosaur relationships and early dinosaur evolution MorphoBank has been particularly important to the Assembling the Tree of Life initiative sponsored by the National Science Foundation. MorphoBank is well-suited to such projects because of its tools for merging taxonomic, character and matrix-based data, as well as its collaborative features. Highlights of this research include a collaborative matrix on mammal evolution published in Science that included over 4,000 phenomic characters scored for over 80 species, a matrix on extant baleen whales featuring nearly 600 images, and more.
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Sedona Canada Principles

The Sedona Canada Principles are a set of authoritative guidelines published by The Sedona Conference to aid members of the Canadian legal community involved in the identification, collection, preservation, review and production of electronically stored information (ESI). The principles were drafted by a small group of lawyers, judges and technologists called the Sedona Working Group 7 or Sedona Canada. Sedona Canada is an offshoot of The Sedona Conference which is an American "non-profit ... research and educational institute dedicated to the advanced study of law and policy in the areas of antitrust law, complex litigation, and intellectual property rights". == Background == Civil procedure in Canada is jurisdictional with each province following its own rules of civil procedure. However, each province must address the fact that due to the advancement of technology the discovery process enshrined in the rules of civil procedure can be potentially derailed due to the sheer volume of electronically stored information (ESI). When dealing with litigation matters that involve electronically stored information (ESI), the discovery process is commonly called e-discovery. The problems associated with e-discovery in Canada led to the creation of the Sedona Canada Principles. Rule 29.1.03(4) of the wikibooks:Ontario Rules of Civil Procedure specifically refers to the Sedona Canada Principles in referencing Principles re Electronic Discovery although it has been reported that this rule has been largely ignored in practice. == Summary == The Sedona Canada Principles largely refer to the processes found in the Electronic Discovery Reference Model. The principles urge proportionality due to the potentially enormous volumes of documents that may be discoverable when dealing with ESI. They also encourage good faith in the document preservation stage and regular meetings between parties to discuss the scope of the litigation. Parties are urged to be aware of the potential costs involved in producing relevant ESI but are advised that only reasonably accessible ESI need be produced. The principles stipulate that parties should not be required to search for or collect deleted material unless there is an agreement or court order related to those terms. The use of electronic tools and processes such as data sampling and web harvesting are acceptable practices. Parties are encouraged to agree early in the litigation process on production format required for the exchange of relevant documents as part of the discovery process (native files, pdf, tiff, metadata requirements etc.). Agreements or direction should be sought, if necessary, with respect to privilege or other confidential information related to production of electronic documents and data. Parties should be aware that legal precedents can be formed as a result of e-discovery practices and sanctions can be considered for a party's failure to meet their discovery obligations unless it can be demonstrated that the failure was not intentional. All parties must bear the “reasonable” costs associated with e-discovery but other arrangements can be agreed upon by the parties or by court order. == Caselaw == In Warman v. National Post Company proportionality was at issue in a case where the plaintiff was suing the defendant for libel. A motion was brought by the defendant to have the plaintiff provide a mirror image of his hard drive in an effort to prove an internet article was indeed authored by the plaintiff. Issues of proportionality and the work of the Sedona Conference and Sedona Canada Principles were factored in to the decision to grant the defendant only limited access to the hard drive. In Innovative Health Group Inc. v. Calgary Health Region the plaintiff's legal obligation to produce imaged hard drives is in question. Justice Conrad refers to the advice of Sedona Canada on proportionality and problems associated with time and expense related to the difficulties associated with electronically stored information. In York University v. Michael Markicevic Justice Brown specifically refers to the need for the parties to agree upon a formal e-discovery plan to be drafted in consultation with Sedona Canada Principles. In Friends of Lansdowne v. Ottawa Master MacLeod refers to the need for Sedona Canada principles and states “This is particularly true in the current information age when e-mail is ubiquitous and multiple copies or variants of messages may be held on various kinds of data storage devices including individual hard drives, e-mail and Blackberry servers. Even documents that ultimately exist in paper form normally begin their life on computers and negotiations frequently involve exchanges of electronic drafts. To find every scrap of paper and every electronic trace of relevant information has become a nightmarish task that threatens to render any kind of litigation extravagantly expensive.” == Criticism == Critics of the Sedona Canada Principles believe they should address system integrity and that the true history of any file preserved cannot be identified without proof of the integrity of the electronic record systems management it comes from. Other criticism is more directed to the Sedona Canada working group and complaints that it is insular and irrelevant.
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Artificial intelligence industry in Canada

The artificial intelligence industry in Canada is a rapidly expanding sector. Although Canada held a pioneering role in the early development of artificial intelligence, transforming research excellence into broad commercial adoption has proven challenging. Despite globally recognized scientific achievements and a deep pool of skilled experts, by June 2024, Canada recorded the lowest rate of AI integration among OECD countries, with only 12% of firms implementing AI in their products or services. However, AI adoption has shown significant momentum—doubling from mid-2024 to mid-2025, rising from 6.1% to 12.2%. As of September 2025, Statistics Canada indicated that while about one-third of Canadian businesses had no plans to adopt artificial intelligence in the next year, 14.5% reported intentions to begin using AI for producing goods or delivering services. The primary reasons for not moving forward with AI were lack of relevance, insufficient knowledge, and privacy concerns. According to Public Works Canada (PwC), the pace of AI adoption in Canada is roughly three-quarters of the United States rate, highlighting a notable gap between the two countries in business integration of this technology. British-Canadian computer scientist Geoffrey Hinton stated in 2025 that Canadian companies are adopting artificial intelligence at a slower pace, which may result in the loss of the country's early advantages in the field. At the "All In AI" conference held in Montreal in September 2025, the Minister of Artificial Intelligence and Digital Innovation Evan Solomon, described "Building digital sovereignty" as the most pressing democratic issue of the time. He introduced a 26-person task force focused on updating Canada's AI strategy. In their 2024 report " "Learning Together for Responsible Artificial Intelligence" report, the Innovation, Science, and Economic Development Canada stressed that public awareness, trust, and AI literacy are essential for the responsible adoption and governance of AI in Canada. Montreal workshops in 2021 expanded the OECD's 2019 definition of AI as "the set of computer techniques that enable a machine (e.g., a computer or telephone) to perform tasks that typically require intelligence, such as reasoning or learning. It is also referred to as the automation of intelligent tasks. Scientific developments in AI, such as deep-learning techniques, have made it possible to design access to huge amounts of data and ever-increasing computing power. These new techniques have been rapidly deployed on a large scale in all areas of social life, in transport, education, culture and health." == Federal investments and policy == The 2025 federal budget allocates over $1 billion over the next five years to bolster Canada's artificial intelligence and quantum computing ecosystem. == Industry landscape or research hubs == AlexNet, an influential deep convolutional neural network developed at the University of Toronto by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, marked a pivotal turning point in modern artificial intelligence. In 2012, it achieved a dramatic reduction in error rates for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), showcasing the practical power of deep learning and GPU acceleration. The success of AlexNet helped cement Canada’s reputation for AI leadership and inspired rapid adoption of deep learning across the technology sector, with ongoing impact in both academic and commercial domains. In healthcare, AlexNet has been adapted for medical imaging to assist with analyzing radiographs, mammograms, and other scans, including identifying abnormalities and supporting clinical diagnosis. In 2015, the Ottawa-based start-up Advanced Symbolics Inc. (ASI) began developing Polly, an artificial intelligence system designed to analyze and anticipate how target audiences behave—enabling more effective communication strategies and advertising campaigns. Polly was named after its first assignment analyzing the politics of Brexit. The AI gained widespread attention in 2016 for accurately forecasting both the Brexit referendum and the 2016 U.S. presidential election won by Donald Trump. The company states that Polly is used by organizations in diverse sectors—including healthcare, politics, entertainment, and mental health research—to support decision-making based on predictive analytics. Chartwatch, an AI tool developed in Canada, has been shown to reduce unexpected hospital deaths by 26% according to a 2024 study. The system analyzes patient data to detect subtle signs of deterioration, supporting healthcare teams in providing timely interventions. === Notable figures in AI in Canada === Geoffrey Hinton's decades-long work eventually formed the foundation of artificial intelligence, which earned him the Nobel Prize for physics in 2024. Yoshua Bengio, who won the Turing Award in 2018 for his pioneering work in deep learning, founded what would become Mila in 1993. Mila, is currently a collaboration between four Montreal-based academic partners.—the Pan-Canadian Artificial Intelligence Strategy includes Alberta's Amii, Toronto's Vector Institute, and Mila. Fakhreddine Karray's work on operational AI has had tangible impact across several Canadian-relevant sectors, notably intelligent transportation systems, virtual healthcare, and driver safety. === AI in the oil and gas industry === According to a 2020 Ernst & Young report the oil and gas industry in Canada is using AI in automating routine, repetitive, and dangerous tasks with technologies like robotic process automation and machine learning; optimizing production and processing; enhancing transportation logistics; improving equipment operation and monitoring; and enabling preventative maintenance. AI is also deployed for data analysis to improve prediction and decision-making, and is expected to automate up to 50% of job competencies in upstream oil and gas by 2040. Oilsands giant Suncor Energy operates a large fleet of autonomous trucks and has started using AI in its dispatch system at the Mildred Lake mine. As of 2024, AI manages routine tasks such as allocating trucks to dump stations and sending them to refuelling locations. === Indigenous and Inuit Innovation in AI === Indigenous organizations have been working on the creation of new technologies for language revitalization in partnership with National Research Council of Canada since the mid-2010s. In 2025, Inuit researchers and technology partners launched an AI-powered initiative to support the revitalization and preservation of Inuktitut, demonstrating how artificial intelligence can be adapted for Indigenous language and cultural priorities. A 2025 CBC article notes that, while AI can help revitalize Inuktitut, Inuit leaders emphasize concerns about data sovereignty, information ownership, and the need for Indigenous leadership to ensure transparency, privacy, and accountability in AI development. == Regulation == Canada's Artificial Intelligence and Data Act (AIDA) was proposed in November 2022, as part of the Digital Charter Implementation Act (Bill C-27). As well voluntary codes, such as the September 2023 Code of Conduct for Generative AI, and landmark investments in advanced computing infrastructure and the Canadian Artificial Intelligence Safety Institute (CAISI) reflect Canada's commitment to both safety and global competitiveness. == AI infrastructure == Canada has undertaken efforts to expand its AI computing infrastructure at both provincial and federal levels. The federal government's Canadian Sovereign AI Compute Strategy, allocated up to C$2 billion in Budget 2024, aims to enhance computing capacity to support domestic AI industry growth and AI adoption across the economy, with up to C$700 million designated to mobilize private sector investment in new or expanded data centres. Alberta has introduced an AI Data Centres Strategy to position itself as a leading North American destination for data centre investment, targeting C$100 billion worth of AI data centres under development by 2030. One major project under Alberta's strategy is the Wonder Valley AI Data Centre Park near Grande Prairie, which was exempted from provincial environmental impact assessment in April 2026 but still requires permits demonstrating safe construction and operation. According to Statista, as of April 2026, Canada has 287 data centres.
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Artificial intelligence in industry

Industrial artificial intelligence, or industrial AI, refers to the application of artificial intelligence to industrial business processes. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis and insight discovery. Artificial intelligence and machine learning have become key enablers to leverage data in production in recent years due to a number of different factors: More affordable sensors and the automated process of data acquisition; More powerful computation capability of computers to perform more complex tasks at a faster speed with lower cost; Faster connectivity infrastructure and more accessible cloud services for data management and computing power outsourcing. == Categories == Possible applications of industrial AI and machine learning in the production domain can be divided into seven application areas: Market and trend analysis Machinery and equipment Intralogistics Production process Supply chain Building Product Each application area can be further divided into specific application scenarios that describe concrete AI/ML scenarios in production. While some application areas have a direct connection to production processes, others cover production adjacent fields like logistics or the factory building. An example from the application scenario Process Design & Innovation are collaborative robots. Collaborative robotic arms are able to learn the motion and path demonstrated by human operators and perform the same task. Predictive and preventive maintenance through data-driven machine learning are application scenarios from the Machinery & Equipment application area. == Challenges == In contrast to entirely virtual systems, in which ML applications are already widespread today, real-world production processes are characterized by the interaction between the virtual and the physical world. Data is recorded using sensors and processed on computational entities and, if desired, actions and decisions are translated back into the physical world via actuators or by human operators. This poses major challenges for the application of ML in production engineering systems. These challenges are attributable to the encounter of process, data and model characteristics: The production domain's high reliability requirements, high risk and loss potential, the multitude of heterogeneous data sources and the non-transparency of ML model functionality impede a faster adoption of ML in real-world production processes. In particular, production data comprises a variety of different modalities, semantics and quality. Furthermore, production systems are dynamic, uncertain and complex, and engineering and manufacturing problems are data-rich but information-sparse. Besides that, due to the variety of use cases and data characteristics, problem-specific data sets are required, which are difficult to acquire, hindering both practitioners and academic researchers in this domain. === Process and industry characteristics === The domain of production engineering can be considered as a rather conservative industry when it comes to the adoption of advanced technology and their integration into existing processes. This is due to high demands on reliability of the production systems resulting from the potentially high economic harm of reduced process effectiveness due to e.g., additional unplanned downtime or insufficient product qualities. In addition, the specifics of machining equipment and products prevent area-wide adoptions across a variety of processes. Besides the technical reasons, the reluctant adoption of ML is fueled by a lack of IT and data science expertise across the domain. === Data characteristics === The data collected in production processes mainly stem from frequently sampling sensors to estimate the state of a product, a process, or the environment in the real world. Sensor readings are susceptible to noise and represent only an estimate of the reality under uncertainty. Production data typically comprises multiple distributed data sources resulting in various data modalities (e.g., images from visual quality control systems, time-series sensor readings, or cross-sectional job and product information). The inconsistencies in data acquisition lead to low signal-to-noise ratios, low data quality and great effort in data integration, cleaning and management. In addition, as a result from mechanical and chemical wear of production equipment, process data is subject to various forms of data drifts. === Machine learning model characteristics === ML models are considered as black-box systems given their complexity and intransparency of input-output relation. This reduces the comprehensibility of the system behavior and thus also the acceptance by plant operators. Due to the lack of transparency and the stochasticity of these models, no deterministic proof of functional correctness can be achieved, complicating the certification of production equipment. Given their inherent unrestricted prediction behavior, ML models are vulnerable against erroneous or manipulated data, further risking the reliability of the production system because of lacking robustness and safety. In addition to high development and deployment costs, the data drifts cause high maintenance costs, which is disadvantageous compared to purely deterministic programs. == Standard processes for data science in production == The development of ML applications – starting with the identification and selection of the use case and ending with the deployment and maintenance of the application – follows dedicated phases that can be organized in standard process models. The process models assist in structuring the development process and defining requirements that must be met in each phase to enter the next phase. The standard processes can be classified into generic and domain-specific ones. Generic standard processes (e.g., CRISP-DM, ASUM-DM, or knowledge discovery in databases (KDD)) describe a generally valid methodology and are thus independent of individual domains. Domain-specific processes on the other hand consider specific peculiarities and challenges of special application areas. The Machine Learning Pipeline in Production is a domain-specific data science methodology that is inspired by the CRISP-DM model and was specifically designed to be applied in fields of engineering and production technology. To address the core challenges of ML in engineering – process, data, and model characteristics – the methodology especially focuses on use-case assessment, achieving a common data and process understanding data integration, data preprocessing of real-world production data and the deployment and certification of real-world ML applications. == Industrial data sources == The foundation of most artificial intelligence and machine learning applications in industrial settings are comprehensive datasets from the respective fields. Those datasets act as the basis for training the employed models. In other domains, like computer vision, speech recognition or language models, extensive reference datasets (e.g. ImageNet, Librispeech, The People's Speech) and data scraped from the open internet are frequently used for this purpose. Such datasets rarely exist in the industrial context because of high confidentiality requirements and high specificity of the data. Industrial applications of artificial intelligence are therefore often faced with the problem of data availability. For these reasons, existing open datasets applicable to industrial applications, often originate from public institutions like governmental agencies or universities and data analysis competitions hosted by companies. In addition to this, data sharing platforms exist. However, most of these platforms have no industrial focus and offer limited filtering abilities regarding industrial data sources.
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World Database of Happiness

The World Database of Happiness is a web-based archive of research findings on subjective appreciation of life, based in the Erasmus Happiness Economics Research Organization of the Erasmus University Rotterdam in The Netherlands. The database contains both an overview of scientific publications on happiness and a digest of research findings. Happiness is defined as the degree to which an individual judges the quality of his or her life as a whole favorably. Two 'components' of happiness are distinguished: hedonic level of affect (the degree to which pleasant affect dominates) and contentment (perceived realization of wants). == Aims == The World Database of Happiness is a tool to quickly acquire an overview on the ever-growing stream of research findings on happiness Medio 2023 the database covered some 16,000 scientific publications on happiness, from which were extracted 23,000 distributional findings (on how happy people are) and another 24,000 correlational findings (on factors associated with more and less happiness). The first findings date from 1915. == Technique == The World Database of Happiness is a ‘findings archive’, which consists of electronic ‘finding pages’ on which separate research results are described in a standard format and terminology. These finding pages can be selected on various characteristics, such as population studies, the measure of happiness used and observed co-variates. All finding-pages have a specific internet address to which links can be made in scientific review papers or policy recommendations. This allows a concise presentation of many findings in a table, while providing readers with access to detail. == Scientific use == The Database has been cited in 254 scientific papers, for example to access under what conditions economic growth enhances average happiness or to show that rising mean happiness at first raises happiness inequality, but further rise will diminish these differences, or that healthy eating is associated with more happiness, even after controlling for the effect on health Another finding is that relative simple happiness training techniques raise happiness by some 5% == Popular use == The World Database of Happiness is often used by popular media to make lists of the happiest countries around the globe. An example is the Happy Planet Index, which aims to chart sustainable happiness all over the world by combining data on longevity, happiness and the size of the ecological footprint of citizens. == Strengths and weaknesses == The database has a clear conceptual focus, it includes only research findings on subjective enjoyment of one's life as a whole. Thereby it evades the Babel that has haunted the study of happiness for ages. The other side of that coin is that much interesting research is left out. The findings are reported with technical details about measurement and statistical analysis. This detail is welcomed by scholars, but makes the information difficult to digest for lay-persons. Still another limitation is that the determinants of happiness appear to vary considerably across persons and situations, which make it hard to draw general conclusions about the causes of happiness. What is clear is that poor health, separation, unemployment and lack of social contact are all strongly negatively associated with happiness. Another problem for the World database of happiness is that the studies on happiness increase with such a high rate that it gets increasingly difficult to offer a complete overview of all research findings. A further concern is that the Database of Happiness is exclusively focused on hedonic happiness (feeling good) and not on mature happiness that might exist in the face of suffering
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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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Open Compute Project

The Open Compute Project (OCP) is an organization that facilitates the sharing of data center product designs and industry best practices among companies. Founded in 2011, OCP has significantly influenced the design and operation of large-scale computing facilities worldwide. As of February 2025, over 400 companies across the world are members of OCP, including Arm, Meta, IBM, Wiwynn, Intel, Nokia, Google, Microsoft, Seagate Technology, Dell, Rackspace, Hewlett Packard Enterprise, NVIDIA, Cisco, Goldman Sachs, Fidelity, Lenovo, Accton Technology Corporation and Alibaba Group. == Structure == The Open Compute Project Foundation is a 501(c)(6) non-profit incorporated in the state of Delaware, United States. OCP has multiple committees, including the board of directors, advisory board and steering committee to govern its operations. As of July 2020, there are seven members who serve on the board of directors which is made up of one individual member and six organizational members. Mark Roenigk (Facebook) is the Foundation's president and chairman. Andy Bechtolsheim is the individual member. In addition to Mark Roenigk who represents Facebook, other organizations on the Open Compute board of directors include Intel (Rebecca Weekly), Microsoft (Kushagra Vaid), Google (Partha Ranganathan), and Rackspace (Jim Hawkins). A list of members can be found on the OCP website. == History == The Open Compute Project began at Facebook (now Meta) in 2009 as an internal project called "Project Freedom". The hardware designs and engineering teams were led by Amir Michael (Manager, Hardware Design) and sponsored by Jonathan Heiliger (VP, Technical Operations) and Frank Frankovsky (Director, Hardware Design and Infrastructure). The three would later open source the designs of Project Freedom and co-found the Open Compute Project. The project was announced at a press event at Facebook's headquarters in Palo Alto on April 7, 2011. == OCP projects == The Open Compute Project Foundation maintains a number of OCP projects, such as: === Server designs === In 2013, two years after the Open Compute Project had started, it was noted that the goal of a more modular server design was "still a long way from live data centers". However, by then some aspects published had been used in Facebook's Prineville data center to improve energy efficiency, as measured by the power usage effectiveness index defined by The Green Grid. Efforts to advance server compute node designs included one for Intel processors and one for AMD processors. Also in 2013, Calxeda contributed a design with ARM architecture processors. Since then, several generations of OCP server designs have been deployed: Wildcat (Intel), Spitfire (AMD), Windmill (Intel E5-2600), Watermark (AMD), Winterfell (Intel E5-2600 v2) and Leopard (Intel E5-2600 v3). === OCP Accelerator Module === OCP Accelerator Module (OAM) is a design specification for hardware architectures that implement artificial intelligence systems that require high module-to-module bandwidth. OAM is used in some of AMD's Instinct accelerator modules. === Rack and power designs === Designs for a mechanical mounting system to replace standard 19-inch racks have been published, with a cabinet the same outside width (600 mm) and depth as existing racks, but with an interior space allowing for wider equipment chassis with a 537 mm width (21 inches). This allows more equipment to fit in the same volume and improves air flow. Compute chassis sizes are defined in multiples of an OpenU or OU, which is 48 mm, slightly taller than the 44 mm rack unit defined for 19-inch racks. As of March 2026, the most current base mechanical definition is the Open Rack V3.1 Specification. At the time the base specification was released, Meta also defined in greater depth the specifications for the rectifiers and power shelf. Specifications for the power monitoring interface (PMI), a communications interface enabling upstream communications between the rectifiers and battery backup unit(BBU) were published by Meta that same year, with Delta Electronics as the main technical contributor to the BBU spec. However, since 2022 the AI boom in the data center has created higher power requirements in order to satisfy the demands of AI accelerators that have been released. As of September 2024, Meta is in the process of updating its Open Rack v3 rectifier, power shelf, battery backup and power management interface specifications to accommodate this increased energy demand. In May 2024, at an Open Compute regional summit, Meta and Rittal outlined their plans for development of their High Power Rack (HPR) ecosystem in conjunction with rack, power and cable partners, increasing power capacity in the rack to 92 kilowatts or more. At the same meeting, Delta Electronics and Advanced Energy reported on their progress in developing new Open Compute standard specifications for power shelf and rectifier designs for HPR applications. Rittal also outlined their collaboration with Meta in designing airflow containment, busbar designs and grounding schemes for the new HPR requirements. === Data storage === Open Vault storage building blocks (also called "Knox") offer high disk densities, with 30 drives in a 2 OU Open Rack chassis designed for easy disk drive replacement. The 3.5 inch disks are stored in two drawers, five across and three deep in each drawer, with connections via serial attached SCSI. There is a "cold storage" variant where idle disks power down to reduce energy consumption. Another design concept was contributed by Hyve Solutions, a division of Synnex, in 2012. At the OCP Summit 2016 Facebook, together with Taiwanese ODM Wistron's spin-off Wiwynn, introduced "Lightning", a flexible NVMe JBOF (just a bunch of flash), based on the existing Open Vault (Knox) design. === Energy efficient data centers === The OCP has published data center designs for energy efficiency. These include power distribution at three-phase 277/480 VAC, which eliminates one transformer stage in typical North American data centers, a single voltage (12.5 VDC) power supply designed to work with 277/480 VAC input, and 48 VDC battery backup. For European (and other 230V countries) datacenters, there is a specification for 230/400 VAC power distribution and its conversion to 12.5 VDC. === Open networking switches === On May 8, 2013, an effort to define an open network switch was announced. The plan was to allow Facebook to load its own operating system software onto its top-of-rack switches. Press reports predicted that more expensive and higher-performance switches would continue to be popular, while less expensive products treated more like a commodity. The first attempt at an open networking switch by Facebook was designed together with Taiwanese ODM Accton using Broadcom Trident II chip and is called "Wedge"; the Linux OS that it runs is called "FBOSS". Later switch contributions include "6-pack" and Wedge-100, based on Broadcom Tomahawk chips. Similar switch hardware designs have been contributed by: Accton Technology Corporation (and its Edgecore Networks subsidiary), Mellanox Technologies, Interface Masters Technologies, Agema Systems. Capable of running Open Network Install Environment (ONIE)-compatible network operating systems such as Cumulus Linux, Switch Light OS by Big Switch Networks, or PICOS by Pica8. A similar project for a custom switch for the Google platform had been rumored, and evolved to use the OpenFlow protocol. === Servers === A sub-project for Mezzanine (NIC) OCP NIC 3.0 specification 1v00 was released in late 2019 establishing three form factors: SFF, TSFF, and LFF. == Litigation == In March, 2015, BladeRoom Group Limited and Bripco (UK) Limited sued Facebook, Emerson Electric Co. and others alleging that Facebook has disclosed BladeRoom and Bripco's trade secrets for prefabricated data centers in the Open Compute Project. Facebook petitioned for the lawsuit to be dismissed, but this was rejected in 2017. A confidential mid-trial settlement was agreed in April 2018.
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Master data management

Master data management (MDM) is a discipline in which business and information technology collaborate to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise's official shared master data assets. == Reasons for master data management == Data consistency and accuracy: MDM ensures that the organization's critical data is consistent and accurate across all systems, reducing discrepancies and errors caused by multiple, siloed copies of the same data. Improved decision-making: By providing a single version of the truth (SVOT), MDM enables organizations to deliver the right data to decision makers, allowing them to clearly understand business performance and make informed, data-driven decisions. Operational efficiency: With the consistent and accurate data provided by an MDM, operational processes such as reporting and inventory management can be automated to improve efficiency. Employee learning, onboarding, and customer service also become more efficient, as MDM data facilitates rapid, accurate, and thorough information retrieval, permitting more employee time to be spent on work. Regulatory compliance: MDM tries to help organizations comply with industry standards and regulations by ensuring that master data is accurately recorded, maintained, and audited. However, issues with data quality, classification, and reconciliation may require data transformation. As with other Extract, Transform, Load-based data movements, these processes are expensive and inefficient, reducing return on investment for a project. == Business unit and product line segmentation == As a result of business unit and product line segmentation, the same entity (whether a customer, supplier, or product) will be included in different product lines. This leads to data redundancy and even confusion. For example, a customer takes out a mortgage at a bank. If the marketing and customer service departments have separate databases, advertisements might still be sent to the customer, even though they've already signed up. The two parts of the bank are unaware, and the customer is sent irrelevant communications. Record linkage can associate different records corresponding to the same entity, mitigating this issue. == Mergers and acquisitions == One of the most common problems for master data management is company growth through mergers or acquisitions. Reconciling these separate master data systems can present difficulties, as existing applications have dependencies on the master databases. Ideally, database administrators resolve this problem through deduplication of the master data as part of the merger. Over time, as further mergers and acquisitions occur, the problem can multiply. Data reconciliation processes can become extremely complex or even unreliable. Some organizations end up with 10, 15, or even 100 separate and poorly integrated master databases. This can cause serious problems in customer satisfaction, operational efficiency, decision support, and regulatory compliance. Another problem involves determining the proper degrees of detail and normalization to include in the master data schema. For example, in a federated Human Resources environment, the enterprise software may focus on storing people's data as current status, adding a few fields to identify the date of hire, date of last promotion, etc. However, this simplification can introduce business-impacting errors into dependent systems for planning and forecasting. The stakeholders of such systems may be forced to build a parallel network of new interfaces to track the onboarding of new hires, planned retirements, and divestment, which works against one of the aims of master data management. == People, processes and technology == Master data management is enabled by technology, but is more than the technologies that enable it. An organization's master data management capability will also include people and processes in its definition. === People === Several roles should be staffed within MDM. Most prominently, the Data Owner and the Data Steward. Several people would likely be allocated to each role and each person responsible for a subset of Master Data (e.g. one data owner for employee master data, another for customer master data). The Data Owner is responsible for the requirements for data definition, data quality, data security, etc. as well as for compliance with data governance and data management procedures. The Data Owner should also be funding improvement projects in case of deviations from the requirements. The Data Steward is running the master data management on behalf of the data owner and probably also being an advisor to the Data Owner. === Processes === Master data management can be viewed as a "discipline for specialized quality improvement" defined by the policies and procedures put in place by a data governance organization. It has the objective of providing processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing master data throughout an organization to ensure a common understanding, consistency, accuracy and control, in the ongoing maintenance and application use of that data. Processes commonly seen in master data management include source identification, data collection, data transformation, normalization, rule administration, error detection and correction, data consolidation, data storage, data distribution, data classification, taxonomy services, item master creation, schema mapping, product codification, data enrichment, hierarchy management, business semantics management and data governance. === Technology === A master data management tool can be used to support master data management by removing duplicates, standardizing data (mass maintaining), and incorporating rules to eliminate incorrect data from entering the system to create an authoritative source of master data. Master data are the products, accounts, and parties for which the business transactions are completed. Where the technology approach produces a "golden record" or relies on a "source of record" or "system of record", it is common to talk of where the data is "mastered". This is accepted terminology in the information technology industry, but care should be taken, both with specialists and with the wider stakeholder community, to avoid confusing the concept of "master data" with that of "mastering data". ==== Implementation models ==== There are several models for implementing a technology solution for master data management. These depend on an organization's core business, its corporate structure, and its goals. These include: Source of record Registry Consolidation Coexistence Transaction/centralized ===== Source of record ===== This model identifies a single application, database, or simpler source (e.g. a spreadsheet) as being the "source of record" (or "system of record" where solely application databases are relied on). The benefit of this model is its conceptual simplicity, but it may not fit with the realities of complex master data distribution in large organizations. The source of record can be federated, for example by groups of attributes (so that different attributes of a master data entity may have different sources of record) or geographically (so that different parts of an organization may have different master sources). Federation is only applicable in certain use cases, where there is a clear delineation of which subsets of records will be found in which sources. The source of record model can be applied more widely than simply to master data, for example to reference data. ==== Transmission of master data ==== There are several ways in which master data may be collated and distributed to other systems. This includes: Data consolidation – The process of capturing master data from multiple sources and integrating it into a single hub (operational data store) for replication to other destination systems. Data federation – The process of providing a single virtual view of master data from one or more sources to one or more destination systems. Data propagation – The process of copying master data from one system to another, typically through point-to-point interfaces in legacy systems. == Change management in implementation == Challenges in adopting master data management within large organizations often arise when stakeholders disagree on a "single version of the truth" concept is not affirmed by stakeholders, who believe that their local definition of the master data is necessary. For example, the product hierarchy used to manage inventory may be entirely different from the product hierarchies used to support marketing efforts or pay sales representatives. It is above all necessary to identify if different master data is genuinely required. If it is required, then the solution implemented (technology and process) must be able to allow multiple versions of the truth to exist but will prov
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Pixel binning

Pixel binning, also known as binning, is a process image sensors of digital cameras use to combine adjacent pixels throughout an image, by summing or averaging their values, during or after readout. It improves low-light performance while still allowing for highly detailed photographs in good light. Charge from adjacent pixels in CCD or charge-coupled device image sensors and some other image sensors can be combined during readout, increasing the line rate or frame rate. In the context of image processing, binning is the procedure of combining clusters of adjacent pixels, throughout an image, into single pixels. For example, in 2×2 binning, an array of 4 pixels becomes a single larger pixel, reducing the number of pixels to 1/4 and halving the image resolution in each dimension. The result can be the sum, average, median, minimum, or maximum value of the cluster. Some systems use more advanced algorithms such as considering the values of nearby pixels, edge detection, self-claimed "AI", etc. to increase the perceived visual quality of the final downsized image. This aggregation, although associated with loss of information, reduces the amount of data to be processed, facilitating analysis. The binned image has lower resolution, but the relative noise level in each pixel is generally reduced. == History == Normally, an increase in megapixel count on a constant image sensor size would lead to a sacrifice of the surface size of the individual pixels, which would result in each pixel being able to catch less light in the same time, thus leading to a darker and/or noisier image in low light (given the same exposure time). In the past, camera manufacturers had to compromise between low-light performance and the amount of detail in good light, by dropping the megapixel count like HTC did in 2013 with their four-megapixel "UltraPixel" camera. However, this results in less detailed images in daylight where enough light is available. With pixel binning, the camera has "the best of both worlds", meaning both the benefit of high detail in good light and the benefit of high brightness in low light. In low light, the surfaces of four or more pixels can act as one large pixel that catches far more light. For example, some smartphones such as the Samsung Galaxy A15 are able to capture photographs with up to fifty megapixels in daylight. However, in low light, the individual pixels would be too small to capture the light needed for a bright image with the short exposure time available for handheld shooting. Therefore, with pixel binning activated, the 50-megapixel image sensor acts as a 12.5-megapixel image sensor, a quarter of its original resolution, with an accordingly larger surface area per pixel.
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Ubiquitous computing

Ubiquitous computing (or "ubicomp") is a concept in software engineering, hardware engineering and computer science where computing is made to appear seamlessly anytime and everywhere. In contrast to desktop computing, ubiquitous computing implies use on any device, in any location, and in any format. A user interacts with the computer, which can exist in many different forms, including laptop computers, tablets, smart phones and terminals in everyday objects such as a refrigerator or a pair of glasses. The underlying technologies to support ubiquitous computing include the Internet, advanced middleware, kernels, operating systems, mobile codes, sensors, microprocessors, new I/Os and user interfaces, computer networks, mobile protocols, global navigational systems, and new materials. This paradigm is also described as pervasive computing, ambient intelligence, or "everyware". Each term emphasizes slightly different aspects. When primarily concerning the objects involved, it is also known as physical computing, the Internet of Things, haptic computing, and "things that think". Rather than propose a single definition for ubiquitous computing and for these related terms, a taxonomy of properties for ubiquitous computing has been proposed, from which different kinds or flavors of ubiquitous systems and applications can be described. Ubiquitous computing themes include: distributed computing, mobile computing, location computing, mobile networking, sensor networks, human–computer interaction, context-aware smart home technologies, and artificial intelligence. == Core concepts == Ubiquitous computing is the concept of using small internet connected and inexpensive computers to help with everyday functions in an automated fashion. Mark Weiser proposed three basic forms for ubiquitous computing devices: Tabs: a wearable device that is approximately a centimeter in size Pads: a hand-held device that is approximately a decimeter in size Boards: an interactive larger display device that is approximately a meter in size Ubiquitous computing devices proposed by Mark Weiser are all based around flat devices of different sizes with a visual display. These conceptual device categories were later implemented at Xerox PARC in experimental systems including the PARCTab, PARCPad, and LiveBoard, which served as early prototypes of handheld, tablet-style, and large interactive display computing environments. Expanding beyond those concepts there is a large array of other ubiquitous computing devices that could exist. == History == Mark Weiser coined the phrase "ubiquitous computing" around 1988, during his tenure as Chief Technologist of the Xerox Palo Alto Research Center (PARC). Both alone and with PARC Director and Chief Scientist John Seely Brown, Weiser wrote some of the earliest papers on the subject, largely defining it and sketching out its major concerns. == Recognizing the effects of extending processing power == Recognizing that the extension of processing power into everyday scenarios would necessitate understandings of social, cultural and psychological phenomena beyond its proper ambit, Weiser was influenced by many fields outside computer science, including "philosophy, phenomenology, anthropology, psychology, post-Modernism, sociology of science and feminist criticism". He was explicit about "the humanistic origins of the 'invisible ideal in post-modernist thought'", referencing as well the ironically dystopian Philip K. Dick novel Ubik. Andy Hopper from Cambridge University UK proposed and demonstrated the concept of "Teleporting" – where applications follow the user wherever he/she moves. Roy Want (now at Google), while at Olivetti Research Ltd, designed the first "Active Badge System", which is an advanced location computing system where personal mobility is merged with computing. Later at Xerox PARC, he designed and built the "PARCTab" or simply "Tab", widely recognized as the world's first Context-Aware computer, which has great similarity to the modern smartphone. Bill Schilit (now at Google) also did some earlier work in this topic, and participated in the early Mobile Computing workshop held in Santa Cruz in 1996. Ken Sakamura of the University of Tokyo, Japan leads the Ubiquitous Networking Laboratory (UNL), Tokyo as well as the T-Engine Forum. The joint goal of Sakamura's Ubiquitous Networking specification and the T-Engine forum, is to enable any everyday device to broadcast and receive information. MIT has also contributed significant research in this field, notably Things That Think consortium (directed by Hiroshi Ishii, Joseph A. Paradiso and Rosalind Picard) at the Media Lab and the CSAIL effort known as Project Oxygen. Other major contributors include University of Washington (Shwetak Patel, Anind Dey and James Landay), Dartmouth College's HealthX Lab (directed by Andrew Campbell), Georgia Tech's College of Computing (Gregory Abowd and Thad Starner), Cornell Tech's People Aware Computing Lab (directed by Tanzeem Choudhury), NYU's Interactive Telecommunications Program, UC Irvine's Department of Informatics, Microsoft Research, Intel Research and Equator, Ajou University UCRi & CUS. == Examples == One of the earliest ubiquitous systems was artist Natalie Jeremijenko's "Live Wire", also known as "Dangling String", installed at Xerox PARC during Mark Weiser's time there. This was a piece of string attached to a stepper motor and controlled by a LAN connection; network activity caused the string to twitch, yielding a peripherally noticeable indication of traffic. Weiser called this an example of calm technology. A present manifestation of this trend is the widespread diffusion of mobile phones. Many mobile phones support high speed data transmission, video services, and other services with powerful computational ability. Although these mobile devices are not necessarily manifestations of ubiquitous computing, there are examples, such as Japan's Yaoyorozu ("Eight Million Gods") Project in which mobile devices, coupled with radio frequency identification tags demonstrate that ubiquitous computing is already present in some form. Ambient Devices has produced an "orb", a "dashboard", and a "weather beacon": these decorative devices receive data from a wireless network and report current events, such as stock prices and the weather, like the Nabaztag, which was invented by Rafi Haladjian and Olivier Mével, and manufactured by the company Violet. The Australian futurist Mark Pesce has produced a highly configurable 52-LED LAMP enabled lamp which uses Wi-Fi named MooresCloud after Gordon Moore. The Unified Computer Intelligence Corporation launched a device called Ubi – The Ubiquitous Computer designed to allow voice interaction with the home and provide constant access to information. Ubiquitous computing research has focused on building an environment in which computers allow humans to focus attention on select aspects of the environment and operate in supervisory and policy-making roles. Ubiquitous computing emphasizes the creation of a human computer interface that can interpret and support a user's intentions. For example, MIT's Project Oxygen seeks to create a system in which computation is as pervasive as air: In the future, computation will be human centered. It will be freely available everywhere, like batteries and power sockets, or oxygen in the air we breathe...We will not need to carry our own devices around with us. Instead, configurable generic devices, either handheld or embedded in the environment, will bring computation to us, whenever we need it and wherever we might be. As we interact with these "anonymous" devices, they will adopt our information personalities. They will respect our desires for privacy and security. We won't have to type, click, or learn new computer jargon. Instead, we'll communicate naturally, using speech and gestures that describe our intent... This is a fundamental transition that does not seek to escape the physical world and "enter some metallic, gigabyte-infested cyberspace" but rather brings computers and communications to us, making them "synonymous with the useful tasks they perform". Network robots link ubiquitous networks with robots, contributing to the creation of new lifestyles and solutions to address a variety of social problems including the aging of population and nursing care. The "Continuity" set of features, introduced by Apple in OS X Yosemite, can be seen as an example of ubiquitous computing. == Issues == Privacy is easily the most often-cited criticism of ubiquitous computing (ubicomp), and may be the greatest barrier to its long-term success. == Research centres == This is a list of notable institutions who claim to have a focus on Ubiquitous computing sorted by country: Canada Topological Media Lab, Concordia University, Canada Finland Community Imaging Group, University of Oulu, Finland Germany Telecooperation Office (TECO), Karlsruhe Institute of Technology, Ger
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Data janitor

A data janitor is a person who works to take big data and condense it into useful amounts of information. Also known as a "data wrangler", a data janitor sifts through data for companies in the information technology industry. A multitude of start-ups rely on large amounts of data, so a data janitor works to help these businesses with this basic, but difficult process of interpreting data. While it is a commonly held belief that data janitor work is fully automated, many data scientists are employed primarily as data janitors. The information technology industry has been increasingly turning towards new sources of data gathered on consumers, so data janitors have become more commonplace in recent years.
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EdgeRank

EdgeRank is the name commonly given to the algorithm that Facebook uses to determine what articles should be displayed in a user's News Feed. As of 2011, Facebook has stopped using the EdgeRank system and uses a machine learning algorithm that, as of 2013, takes more than 100,000 factors into account. EdgeRank was developed and implemented by Serkan Piantino. == Formula and factors == In 2010, a simplified version of the EdgeRank algorithm was presented as: ∑ e d g e s e u e w e d e {\displaystyle \sum _{\mathrm {edges\,} e}u_{e}w_{e}d_{e}} where: u e {\displaystyle u_{e}} is user affinity. w e {\displaystyle w_{e}} is how the content is weighted. d e {\displaystyle d_{e}} is a time-based decay parameter. User Affinity: The User Affinity part of the algorithm in Facebook's EdgeRank looks at the relationship and proximity of the user and the content (post/status update). Content Weight: What action was taken by the user on the content. Time-Based Decay Parameter: New or old. Newer posts tend to hold a higher place than older posts. Some of the methods that Facebook uses to adjust the parameters are proprietary and not available to the public. A study has shown that it is possible to hypothesize a disadvantage of the "like" reaction and advantages of other interactions (e.g., the "haha" reaction or "comments") in content algorithmic ranking on Facebook. The "like" button can decrease the organic reach as a "brake effect of viral reach". The "haha" reaction, "comments" and the "love" reaction could achieve the highest increase in total organic reach. == Impact == EdgeRank and its successors have a broad impact on what users actually see out of what they ostensibly follow: for instance, the selection can produce a filter bubble (if users are exposed to updates which confirm their opinions etc.) or alter people's mood (if users are shown a disproportionate amount of positive or negative updates). As a result, for Facebook pages, the typical engagement rate is less than 1% (or less than 0.1% for the bigger ones), and organic reach 10% or less for most non-profits. As a consequence, for pages, it may be nearly impossible to reach any significant audience without paying to promote their content.
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The Master Algorithm

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World is a book by Pedro Domingos released in 2015. Domingos wrote the book in order to generate interest from people outside the field. == Overview == The book outlines five approaches of machine learning: inductive reasoning, connectionism, evolutionary computation, Bayes' theorem and analogical modelling. The author explains these tribes to the reader by referring to more understandable processes of logic, connections made in the brain, natural selection, probability and similarity judgments. Throughout the book, it is suggested that each different tribe has the potential to contribute to a unifying "master algorithm". Towards the end of the book the author pictures a "master algorithm" in the near future, where machine learning algorithms asymptotically grow to a perfect understanding of how the world and people in it work. Although the algorithm doesn't yet exist, he briefly reviews his own invention of the Markov logic network. == In the media == In 2016 Bill Gates recommended the book, alongside Nick Bostrom's Superintelligence, as one of two books everyone should read to understand AI. In 2018 the book was noted to be on Chinese Communist Party general secretary Xi Jinping's bookshelf. === Reception === A computer science educator stated in Times Higher Education that the examples are clear and accessible. In contrast, The Economist agreed Domingos "does a good job" but complained that he "constantly invents metaphors that grate or confuse". Kirkus Reviews praised the book, stating that "Readers unfamiliar with logic and computer theory will have a difficult time, but those who persist will discover fascinating insights." A New Scientist review called it "compelling but rather unquestioning".
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EJB QL

EJB QL or EJB-QL is a portable database query language for Enterprise Java Beans. It was used in Java EE applications. Compared to SQL, however, it is less complex but less powerful as well. == History == The language has been inspired, especially EJB3-QL, by the native Hibernate Query Language. In EJB3 It has been mostly replaced by the Java Persistence Query Language. == Differences == EJB QL is a database query language similar to SQL. The used queries are somewhat different from relational SQL, as it uses a so-called "abstract schema" of the enterprise beans instead of the relational model. In other words, EJB QL queries do not use tables and their components, but enterprise beans, their persistent state, and their relationships. The result of an SQL query is a set of rows with a fixed number of columns. The result of an EJB QL query is either a single object, a collection of entity objects of a given type, or a collection of values retrieved from CMP fields. One has to understand the data model of enterprise beans in order to write effective queries.
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Scriptella

Scriptella is an open source extract transform load (ETL) and script execution tool written in Java. It allows the use of SQL or another scripting language suitable for the data source to perform required transformations. Scriptella does not offer any graphical user interface. == Typical use == Database migration. Database creation/update scripts. Cross-database ETL operations, import/export. Alternative for Ant task. Automated database schema upgrade. == Features == Simple XML syntax for scripts. Add dynamics to your existing SQL scripts by creating a thin wrapper XML file: Support for multiple datasources (or multiple connections to a single database) in an ETL file. Support for many useful JDBC features, e.g. parameters in SQL including file blobs and JDBC escaping. Performance and low memory usage are one of the primary goals. Support for evaluated expressions and properties (JEXL syntax) Support for cross-database ETL scripts by using elements Transactional execution Error handling via elements Conditional scripts/queries execution (similar to Ant if/unless attributes but more powerful) Easy-to-Use as a standalone tool or Ant task, without deployment or installation. Easy-To-Run ETL files directly from Java code. Built-in adapters for popular databases for a tight integration. Support for any database with JDBC/ODBC compliant driver. Service Provider Interface (SPI) for interoperability with non-JDBC DataSources and integration with scripting languages. Out of the box support for JSR 223 (Scripting for the Java Platform) compatible languages. Built-in CSV, TEXT, XML, LDAP, Lucene, Velocity, JEXL and Janino providers. Integration with Java EE, Spring Framework, JMX and JNDI for enterprise ready scripts.
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