AI Data Delivery F5

AI Data Delivery F5 — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Video imprint (computer vision)

    Video imprint (computer vision)

    Proposed as an extension of image epitomes in the field of video content analysis, video imprint is obtained by recasting video contents into a fixed-sized tensor representation regardless of video resolution or duration. Specifically, statistical characteristics are retained to some degrees so that common video recognition tasks can be carried out directly on such imprints, e.g., event retrieval, temporal action localization. It is claimed that both spatio-temporal interdependences are accounted for and redundancies are mitigated during the computation of video imprints. The option of computing video imprints exploiting the epitome model has the advantage of more flexible input feature formats and more efficient training stage for video content analysis.

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  • Browsing

    Browsing

    Browsing is a kind of orienting strategy. It is supposed to identify something of relevance for the browsing organism. In context of humans, it is a metaphor taken from the animal kingdom. It is used, for example, about people browsing open shelves in libraries, window shopping, or browsing databases or the Internet. In library and information science, it is an important subject, both purely theoretically and as applied science aiming at designing interfaces which support browsing activities for the user. == Definition == In 2011, Birger Hjørland provided the following definition: "Browsing is a quick examination of the relevance of a number of objects which may or may not lead to a closer examination or acquisition/selection of (some of) these objects. It is a kind of orienting strategy that is formed by our "theories", "expectations" and "subjectivity". == Controversies == As with any kind of human psychology, browsing can be understood in biological, behavioral, or cognitive terms on the one hand or in social, historical, and cultural terms on the other hand. In 2007, Marcia Bates researched browsing from "behavioural" approaches, while Hjørland (2011a+b) defended a social view. Bates found that browsing is rooted in our history as exploratory, motile animals hunting for food and nesting opportunities. According to Hjørland (2011a), on the other hand, Marcia Bates' browsing for information about browsing is governed by her behavioral assumptions, while Hjørland's browsing for information about browsing is governed by his socio-cultural understanding of human psychology. In short: Human browsing is based on our conceptions and interests. === Is browsing a random activity? === Browsing is often understood as a random activity. Dictionary.com, for example, has this definition: "to glance at random through a book, magazine, etc.". Hjørland suggests, however, that browsing is an activity that is governed by our metatheories. We may dynamically change our theories and conceptions but when we browse, the activity is governed by the interests, conceptions, priorities and metatheories that we have at that time. Therefore, browsing is not totally random. == Browsing versus analytical search strategies == In 1997, Gary Marchionini wrote: "A fundamental distinction is made between analytical and browsing strategies [...]. Analytical strategies depend on careful planning, the recall of query terms, and iterative query reformulations and examinations of results. Browsing strategies are heuristic and opportunistic and depend on recognizing relevant information. Analytic strategies are batch oriented and half duplex (turn talking) like human conversation, whereas browsing strategies are more interactive, real-time exchanges and collaborations between the information seeker and the information system. Browsing strategies demand a lower cognitive load in advance and a steadier attentional load throughout the information-seeking process. When it comes to Browsing, giblets are amazing." == Orienting strategies == Some sociologists, such as Berger and Zelditch in 1993, Wagner in 1984, and Wagner & Berger in 1985, have used the term "orienting strategies". They find that orienting strategies should be understood as metatheories: "Consider the very large proportion of sociological theory that is in the form of metatheory. It is discussion about theory: about what concepts it should include, about how those concepts should be linked, and about how theory should be studied. Similar to Kuhn’s paradigms, theories of this sort provide guidelines or strategies for understanding social phenomena and suggest the proper orientation of the theorist to these phenomena; they are orienting strategies. Textbooks in theory frequently focus on orienting strategies such as functionalism, exchange, or ethnomethodology." Sociologists thus use metatheories as orienting strategies. We may generalize and say that all people use metatheories as orienting strategies and that this is what direct our attention and also our browsing – also when we are not conscious about it.

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  • Kunstweg

    Kunstweg

    Bürgi's Kunstweg is a set of algorithms developed by Jost Bürgi in the late 16th century. They are used to calculate sines to arbitrary precision.. Bürgi used these algorithms to calculate a Canon Sinuum, a sine table in increments of 2 arc seconds. It is believed that the table featured values accurate to eight sexagesimal places. Some authors have speculated that the table only covered the range from 0° to 45°, although there is no evidence supporting this claim. Such tables were crucial for maritime navigation. Johannes Kepler described the Canon Sinuum as the most precise sine table known at the time. Bürgi explained his algorithms in his work Fundamentum Astronomiae, which he presented to Emperor Rudolf II in 1592. The Kunstweg algorithm calculates sine values iteratively. In each step, the value of a cell is the sum of the two preceding cells in the same column. The final cell's value is halved before beginning the next iteration. Ultimately, the values in the last column are normalized. Accurate sine approximations are achieved after only a few iterations. In 2015, Menso Folkerts and coworkers demonstrated that this iterative process does indeed converge toward the true sine values. According to them this was the first step towards differential calculus.

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  • Online analytical processing

    Online analytical processing

    In computing, online analytical processing (OLAP) (), is an approach to quickly answer multi-dimensional analytical (MDA) queries. The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture. OLAP tools enable users to analyse multidimensional data interactively from multiple perspectives. OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing. Consolidation involves the aggregation of data that can be accumulated and computed in one or more dimensions. For example, all sales offices are rolled up to the sales department or sales division to anticipate sales trends. By contrast, the drill-down is a technique that allows users to navigate through the details. For instance, users can view the sales by individual products that make up a region's sales. Slicing and dicing is a feature whereby users can take out (slicing) a specific set of data of the OLAP cube and view (dicing) the slices from different viewpoints. These viewpoints are sometimes called dimensions (such as looking at the same sales by salesperson, or by date, or by customer, or by product, or by region, etc.). Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad hoc queries with a rapid execution time. They borrow aspects of navigational databases, hierarchical databases and relational databases. OLAP is typically contrasted to OLTP (online transaction processing), which is generally characterized by much less complex queries, in a larger volume, to process transactions rather than for the purpose of business intelligence or reporting. Whereas OLAP systems are mostly optimized for read, OLTP has to process all kinds of queries (read, insert, update and delete). == Overview of OLAP systems == At the core of any OLAP system is an OLAP cube (also called a 'multidimensional cube' or a hypercube). It consists of numeric facts called measures that are categorized by dimensions. The measures are placed at the intersections of the hypercube, which is spanned by the dimensions as a vector space. The usual interface to manipulate an OLAP cube is a matrix interface, like Pivot tables in a spreadsheet program, which performs projection operations along the dimensions, such as aggregation or averaging. The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. Each measure can be thought of as having a set of labels, or meta-data associated with it. A dimension is what describes these labels; it provides information about the measure. A simple example would be a cube that contains a store's sales as a measure, and Date/Time as a dimension. Each Sale has a Date/Time label that describes more about that sale. For example: Sales Fact Table +-------------+----------+ | sale_amount | time_id | +-------------+----------+ Time Dimension | 930.10| 1234 |----+ +---------+-------------------+ +-------------+----------+ | | time_id | timestamp | | +---------+-------------------+ +---->| 1234 | 20080902 12:35:43 | +---------+-------------------+ === Multidimensional databases === Multidimensional structure is defined as "a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data". The structure is broken into cubes and the cubes are able to store and access data within the confines of each cube. "Each cell within a multidimensional structure contains aggregated data related to elements along each of its dimensions". Even when data is manipulated it remains easy to access and continues to constitute a compact database format. The data still remains interrelated. Multidimensional structure is quite popular for analytical databases that use online analytical processing (OLAP) applications. Analytical databases use these databases because of their ability to deliver answers to complex business queries swiftly. Data can be viewed from different angles, which gives a broader perspective of a problem unlike other models. === Aggregations === It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time required for the same query on OLTP relational data. The most important mechanism in OLAP which allows it to achieve such performance is the use of aggregations. Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions, using an aggregate function (or aggregation function). The number of possible aggregations is determined by every possible combination of dimension granularities. The combination of all possible aggregations and the base data contains the answers to every query which can be answered from the data. Because usually there are many aggregations that can be calculated, often only a predetermined number are fully calculated; the remainder are solved on demand. The problem of deciding which aggregations (views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time. View selection is NP-complete. Many approaches to the problem have been explored, including greedy algorithms, randomized search, genetic algorithms and A search algorithm. Some aggregation functions can be computed for the entire OLAP cube by precomputing values for each cell, and then computing the aggregation for a roll-up of cells by aggregating these aggregates, applying a divide and conquer algorithm to the multidimensional problem to compute them efficiently. For example, the overall sum of a roll-up is just the sum of the sub-sums in each cell. Functions that can be decomposed in this way are called decomposable aggregation functions, and include COUNT, MAX, MIN, and SUM, which can be computed for each cell and then directly aggregated; these are known as self-decomposable aggregation functions. In other cases, the aggregate function can be computed by computing auxiliary numbers for cells, aggregating these auxiliary numbers, and finally computing the overall number at the end; examples include AVERAGE (tracking sum and count, dividing at the end) and RANGE (tracking max and min, subtracting at the end). In other cases, the aggregate function cannot be computed without analyzing the entire set at once, though in some cases approximations can be computed; examples include DISTINCT COUNT, MEDIAN, and MODE; for example, the median of a set is not the median of medians of subsets. These latter are difficult to implement efficiently in OLAP, as they require computing the aggregate function on the base data, either computing them online (slow) or precomputing them for possible rollouts (large space). == Types == OLAP systems have been traditionally categorized using the following taxonomy. === Multidimensional OLAP (MOLAP) === MOLAP (multi-dimensional online analytical processing) is the classic form of OLAP and is sometimes referred to as just OLAP. MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database. Some MOLAP tools require the pre-computation and storage of derived data, such as consolidations – the operation known as processing. Such MOLAP tools generally utilize a pre-calculated data set referred to as a data cube. The data cube contains all the possible answers to a given range of questions. As a result, they have a very fast response to queries. On the other hand, updating can take a long time depending on the degree of pre-computation. Pre-computation can also lead to what is known as data explosion. Other MOLAP tools, particularly those that implement the functional database model do not pre-compute derived data but make all calculations on demand other than those that were previously requested and stored in a cache. Advantages of MOLAP Fast query performance due to optimized storage, multidimensional indexing and caching. Smaller on-disk size of data compared to data stored in relational database due to compression techniques. Automated computation of higher-level aggregates of the data. It is very compact for low dimension data se

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  • Supersampling

    Supersampling

    Supersampling or supersampling anti-aliasing (SSAA) is a spatial anti-aliasing method, i.e. a method used to remove aliasing (jagged and pixelated edges, colloquially known as "jaggies") from images rendered in computer games or other computer programs that generate imagery. Aliasing occurs because unlike real-world objects, which have continuous smooth curves and lines, a computer screen shows the viewer a large number of small squares. These pixels all have the same size, and each one has a single color. A line can only be shown as a collection of pixels, and therefore appears jagged unless it is perfectly horizontal or vertical. The aim of supersampling is to reduce this effect. Color samples are taken at several instances inside the pixel (not just at the center as normal)—hence the term "supersampling"—and an average color value is calculated. This can for example be achieved by rendering the image at a much higher resolution than the one being displayed, then shrinking it to the desired size, using the extra pixels for calculation, with the result being a downsampled image with smoother transitions from one line of pixels to another along the edges of objects, but each pixel could also be supersampled using other strategies (see the Supersampling patterns section). The number of samples determines the quality of the output. == Motivation == Aliasing is manifested in the case of 2D images as moiré pattern and pixelated edges, colloquially known as "jaggies". Common signal processing and image processing knowledge suggests that to achieve perfect elimination of aliasing, proper spatial sampling at the Nyquist rate (or higher) after applying a 2D Anti-aliasing filter is required. As this approach would require a forward and inverse fourier transformation, computationally less demanding approximations like supersampling were developed to avoid domain switches by staying in the spatial domain ("image domain"). == Method == === Computational cost and adaptive supersampling === Supersampling is computationally expensive because it requires much greater video card memory and memory bandwidth, since the amount of buffer used is several times larger. A way around this problem is to use a technique known as adaptive supersampling, where only pixels at the edges of objects are supersampled. Initially only a few samples are taken within each pixel. If these values are very similar, only these samples are used to determine the color. If not, more are used. The result of this method is that a higher number of samples are calculated only where necessary, thus improving performance. === Supersampling patterns === When taking samples within a pixel, the sample positions have to be determined in some way. Although the number of ways in which this can be done is infinite, there are a few ways which are commonly used. ==== Grid ==== The simplest algorithm. The pixel is split into several sub-pixels, and a sample is taken from the center of each. It is fast and easy to implement. Although, due to the regular nature of sampling, aliasing can still occur if a low number of sub-pixels is used. ==== Random ==== Also known as stochastic sampling, it avoids the regularity of grid supersampling. However, due to the irregularity of the pattern, samples end up being unnecessary in some areas of the pixel and lacking in others. ==== Poisson disk ==== The Poisson disk sampling algorithm places the samples randomly, but then checks that any two are not too close. The end result is an even but random distribution of samples. The naive "dart throwing" algorithm is extremely slow for large data sets, which once limited its applications for real-time rendering. However, many fast algorithms now exist to generate Poisson disk noise, even those with variable density. The Delone set provides a mathematical description of such sampling. ==== Jittered ==== A modification of the grid algorithm to approximate the Poisson disk. A pixel is split into several sub-pixels, but a sample is not taken from the center of each, but from a random point within the sub-pixel. Congregation can still occur, but to a lesser degree. ==== Rotated grid ==== A 2×2 grid layout is used but the sample pattern is rotated to avoid samples aligning on the horizontal or vertical axis, greatly improving antialiasing quality for the most commonly encountered cases. For an optimal pattern, the rotation angle is arctan (⁠1/2⁠) (about 26.6°) and the square is stretched by a factor of ⁠√5/2⁠, making it also a 4-queens solution.

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  • System of record

    System of record

    A system of record (SOR) or source system of record (SSoR) is a data management term for an information storage system (commonly implemented on a computer system running a database management system) that is the authoritative data source for a given data element or piece of information, like for example a row (or record) in a table. In data vault it is referred to as the record source. == Background == The need to identify systems of record can become acute in organizations where management information systems have been built by taking output data from multiple source systems, re-processing this data, and then re-presenting the result for a new business use. In these cases, multiple information systems may disagree about the same piece of information. These disagreements may stem from semantic differences, differences in opinion, use of different sources, differences in the timing of the extract, transform, load processes that create the data they report against, or may simply be the result of bugs. == Use == The integrity and validity of any data set is open to question when there is no traceable connection to a good source, and listing a source system of record is a solution to this. Where the integrity of the data is vital, if there is an agreed system of record, the data element must either be linked to, or extracted directly from it. In other cases, the provenance and estimated data quality should be documented. The "system of record" approach is a good fit for environments where both: there is a single authority over all data consumers, and all consumers have similar needs == Trade-offs == In diverse environments, one instead needs to support the presence of multiple opinions. Consumers may accept different authorities or may differ on what constitutes an authoritative source—researchers may prefer carefully vetted data, while tactical military systems may require the most recent credible report.

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  • Enterprise information integration

    Enterprise information integration

    Enterprise information integration (EII) is the ability to support a unified view of data and information for an entire organization. The goal of EII is to get a large set of heterogeneous data sources to appear to a user or system as a single, homogeneous data source. In a data virtualization application of EII, there is a process of information integration, using data abstraction to provide a unified interface (known as uniform data access) for viewing all the data within an organization, and a single set of structures and naming conventions (known as uniform information representation) to represent this data. == Overview == Data within an enterprise can be stored in heterogeneous formats, including relational databases (which themselves come in a large number of varieties), text files, XML files, spreadsheets and a variety of proprietary storage methods, each with their own indexing and data access methods. Standardized data access APIs have emerged that offer a specific set of commands to retrieve and modify data from a generic data source. Many applications exist that implement these APIs' commands across various data sources, most notably relational databases. Such APIs include ODBC, JDBC, XQJ, OLE DB, and more recently ADO.NET. There are also standard formats for representing data within a file that are very important to information integration. The best-known of these is XML, which has emerged as a standard universal representation format. There are also more specific XML "grammars" defined for specific types of data such as Geography Markup Language for expressing geographical features and Directory Service Markup Language for holding directory-style information. In addition, non-XML standard formats exist such as iCalendar for representing calendar information and vCard for business card information. Enterprise Information Integration (EII) applies data integration commercially. Despite the theoretical problems described above, the private sector shows more concern with the problems of data integration as a viable product. EII emphasizes neither correctness nor tractability, but speed and simplicity. === Uniform data access === Uniform data access means connectivity and controllability across numerous target data sources. Necessary to fields such as EII and Electronic Data Interchange (EDI), it is most often used regarding analysis of disparate data types and data sources, which must be rendered into a uniform information representation, and generally must appear homogenous to the analysis tools—when the data being analyzed is typically heterogeneous and widely varying in size, type, and original representation. === Uniform information representation === Uniform information representation allows information from several realms or disciplines to be displayed and worked with as if it came from the same realm or discipline. It takes information from a number of sources, which may have used different methodologies and metrics in their data collection, and builds a single large collection of information, where some records may be more complete than others across all fields of data Uniform information representation is particularly important in EII and Electronic Data Interchange (EDI), where different departments of a large organization may have collected information for different purposes, with different labels and units, until one department realized that data already collected by those other departments could be re-purposed for their own needs—saving the enterprise the effort and cost of re-collecting the same information. === Combining disparate data sets === Each data source is disparate and as such is not designed to support EII. Therefore, data virtualization as well as data federation depends upon accidental data commonality to support combining data and information from disparate data sets. Because of this lack of data value commonality across data sources, the return set may be inaccurate, incomplete, and impossible to validate. One solution is to recast disparate databases to integrate these databases without the need for ETL. The recast databases support commonality constraints where referential integrity may be enforced between databases. The recast databases provide designed data access paths with data value commonality across databases. === Simplicity of deployment === Even if recognized as a solution to a problem, EII as of 2009 currently takes time to apply and offers complexities in deployment. Proposed schema-less solutions include "Lean Middleware". === Handling higher-order information === Analysts experience difficulty—even with a functioning information integration system—in determining whether the sources in the database will satisfy a given application. Answering these kinds of questions about a set of repositories requires semantic information like metadata and/or ontologies. == Applications == EII products enable loose coupling between homogeneous-data consuming client applications and services and heterogeneous-data stores. Such client applications and services include Desktop Productivity Tools (spreadsheets, word processors, presentation software, etc.), development environments and frameworks (Java EE, .NET, Mono, SOAP or RESTful Web services, etc.), business intelligence (BI), business activity monitoring (BAM) software, enterprise resource planning (ERP), Customer relationship management (CRM), business process management (BPM and/or BPEL) Software, and web content management (CMS). == Data access technologies == Service Data Objects (SDO) for Java, C++ and .Net clients and any type of data source XQuery and XQuery API for Java

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  • Conceptualization (information science)

    Conceptualization (information science)

    In information science, a conceptualization is an abstract simplified view of some selected parts of the world, containing the objects, concepts, and other entities that are presumed of interest for some particular purpose and the relationships between them. An explicit specification of a conceptualization is an ontology, and it may occur that a conceptualization can be realized by several distinct ontologies. An ontological commitment in describing ontological comparisons is taken to refer to that subset of elements of an ontology shared with all the others. "An ontology is language-dependent", its objects and interrelations described within the language it uses, while a conceptualization is always the same, more general, its concepts existing "independently of the language used to describe it". The relation between these terms is shown in the figure to the right. Not all workers in knowledge engineering use the term "conceptualization", but instead refer to the conceptualization itself, or to the ontological commitment of all its realizations, as an overarching ontology. == Purpose and implementation == As a higher level abstraction, a conceptualization facilitates the discussion and comparison of its various ontologies, facilitating knowledge sharing and reuse. Each ontology based upon the same overarching conceptualization maps the conceptualization into specific elements and their relationships. The question then arises as to how to describe the "conceptualization" in terms that can encompass multiple ontologies. This issue has been called the Tower of Babel problem, that is, how can persons used to one ontology talk with others using a different ontology? This problem is easily grasped, but a general resolution is not at hand. It can be a "bottom-up" or a "top-down" approach, or something in between. However, in more artificial situations, such as information systems, the idea of a "conceptualization" and the "ontological commitment" of various ontologies that realize the "conceptualization" is possible. The formation of a conceptualization and its ontologies involves these steps: specification of the conceptualization ontology concepts: every definition involves the definitions of other terms relationships between the concepts: this step maps conceptual relationships onto the ontology structure groups of concepts: this step may lead to the creation of sub-ontologies formal description of ontology commitments, for example, to make them computer readable An example of moving conception into a language leading to a variety of ontologies is the expression of a process in pseudocode (a strictly structured form of ordinary language) leading to implementation in several different formal computer languages like Lisp or Fortran. The pseudocode makes it easier to understand the instructions and compare implementations, but the formal languages make possible the compilation of the ideas as computer instructions. Another example is mathematics, where a very general formulation (the analog of a conceptualization) is illustrated with "applications" that are more specialized examples. For instance, aspects of a function space can be illustrated using a vector space or a topological space that introduce interpretations of the "elements" of the conceptualization and additional relationships between them but preserve the connections required in the function space.

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  • Puck App

    Puck App

    Puck App is a mobile application that allows hockey players to quickly find and rent a hockey goalie. Founded in 2015 in Toronto, the application primarily operates throughout Canada. It is available on Apple's App Store and Google Play. == History == Puck App was founded in 2016 by Niki Sawni. Users can rate the goalies, message with available goalies, and coordinate skill levels. In 2017, Puck App expanded to Western Canada and has over 1,000 goalies registered. In 2018, Puck App charged approximately $40 CDN to rent a goalie with more than 2 hours notice. Previously, Puck App was a competitor to a similar application called GoalieUp. As of 2024, both companies have agreed to a merger deal.

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  • AI-driven design automation

    AI-driven design automation

    AI-driven design automation is the use of artificial intelligence (AI) to automate and improve different parts of the electronic design automation (EDA) process. It is particularly important in the design of integrated circuits (chips) and complex electronic systems, where it can potentially increase productivity, decrease costs, and speed up design cycles. AI Driven Design Automation uses several methods, including machine learning, expert systems, and reinforcement learning. These are used for many tasks, from planning a chip's architecture and logic synthesis to its physical design and final verification. == History == === 1980s–1990s: Expert systems and early experiments === The use of AI for design automation originated in the 1980s and 1990s, mainly with the creation of expert systems. These systems tried to capture the knowledge and practical rules used by human design experts, and used these rules, along with reasoning engines, to direct the design process. A notable early project was the ULYSSES system from Carnegie Mellon University. ULYSSES was a CAD tool integration environment that let expert designers turn their design methods into scripts that could be run automatically. It treated design tools as sources of knowledge that a scheduler could manage. Another example was the ADAM (Advanced Design AutoMation) system at the University of Southern California, which used an expert system called the Design Planning Engine. This engine figured out design strategies on the fly and handled different design jobs by organizing specialized knowledge into structured formats called frames. Other systems like DAA (Design Automation Assistant) used a rule-based approach for specific jobs, such as register transfer level (RTL) design for systems like the IBM 370. Researchers at Carnegie Mellon University also created TALIB, an expert system for mask layout that used over 1200 rules, and EMUCS/DAA for CPU architectural design which used about 70 rules. These projects showed that AI worked better for problems where relatively few rules were required to describe much larger amounts of data. At the same time, there was a surge of tools called silicon compilers like MacPitts, Arsenic, and Palladio. They used algorithms and search techniques to explore different design paradigms. This was another way to automate design, even if it was not always based on expert systems. Early tests with neural networks in VLSI design also happened during this time, although they were not as common as systems based on rules. === 2000s: Introduction of machine learning === In the 2000s, interest in AI for design automation increased. This was mostly because of better machine learning (ML) algorithms and more available data from design and manufacturing. For example, they were used to model and reduce the effects of small manufacturing differences in semiconductor devices. This became very important as the size of components on chips became smaller. The large amount of data created during chip design provided the foundation needed to train smarter ML models. This allowed for predicting outcomes and optimizing in areas that were hard to automate before. === 2016–2020: Reinforcement learning and large scale initiatives === A major turning point happened in the mid to late 2010s, sparked by successes in other areas of AI. The success of DeepMind's AlphaGo in mastering the game of Go inspired researchers. They began to apply reinforcement learning (RL) to difficult EDA problems. These problems often require searching through many options and making a series of decisions. In 2018, the U.S. DARPA started the Intelligent Design of Electronic Assets (IDEA) program. A main goal of IDEA was to create a fully automated layout generator that required no human intervention, able to produce a chip design ready for manufacturing from RTL specifications in 24 hours. Another big initiative was the OpenROAD project, a large effort under IDEA led by UC San Diego with industry and university partners, aimed to build an open source, independent toolchain. It used machine learning, parallelization and divide and conquer approaches. A much-publicized but controversial demonstration of RL's potential came from Google researchers between 2020 and 2021. They created a deep reinforcement learning method for planning the layout of a chip, known as floorplanning. They reported that this method created layouts that were as good as or better than those made by human experts, and it did so in less than six hours. This method used a type of network called a graph convolutional neural network. It showed that it could learn general patterns that could be applied to new problems, getting better as it saw more chip designs. The technology was later used to design Google's Tensor Processing Unit (TPU) accelerators. However, in the original paper, the improvement (if any) from AI was not demonstrated. There was no comparison with existing non-AI tools performing the same task, and since the data is proprietary, no ability for anyone else to perform this comparison. Various efforts to reproduce the AI algorithm, and compare its results with various commercial and academic tools, have yielded mixed results with no conclusive advantage to AI. === 2020s: Autonomous systems and agents === Entering the 2020s, the industry saw the commercial launch of autonomous AI driven EDA systems. For example, Synopsys launched DSO.ai (Design Space Optimization AI) in early 2020, calling it the first autonomous artificial intelligence application for chip design in the industry. This system uses reinforcement learning to search for the best ways to optimize a design within the huge number of possible solutions, trying to improve power, performance, and area (PPA). By 2023, DSO.ai had been used to produce over 100 commercial chips, showing mainstream adoption. Synopsys later grew its AI tools into a suite called Synopsys.ai. The goal was to use AI in the entire EDA workflow, including verification and testing. These advancements, which combine modern AI methods with cloud computing and large data resources, have led to talks about a new phase in EDA. Industry experts and participants sometimes call this 'EDA 4.0'. This new era is defined by the widespread use of AI and machine learning to deal with growing design complexity, automate more of the design process, and help engineers handle the huge amounts of data that EDA tools create. The purpose of EDA 4.0 is to optimize product performance, get products to market faster and make development and manufacturing smoother through intelligent automation. == Applications == Artificial intelligence (AI) is now used in many stages of the electronic design workflow. It aims to improve productivity, get better results, and handle the growing complexity of modern integrated circuits. AI helps designers from the very first ideas about architecture all the way to manufacturing and testing. === High level synthesis and architectural exploration === In the first phases of chip design, AI helps with High Level Synthesis (HLS) and exploring different system level design options (DSE). These processes are key for turning general ideas into detailed hardware plans. AI algorithms, often using supervised learning, are used to build simpler, substitute models. These models can quickly guess important design measurements like area, performance, and power for many different architectural options or HLS settings. For example, the Ithemal tool uses deep neural networks to estimate how fast basic code blocks will run, which helps in making processor architecture decisions. Similarly, PRIMAL uses machine learning estimate power use at the register transfer level (RTL), giving early information about how much power the chip will use. Reinforcement learning (RL) and Bayesian optimization are also used to guide the DSE process. They help search through the many parameters to find the best HLS settings or architectural details like cache sizes. LLMs are also being tested for creating architectural plans or initial C code for HLS, as seen with GPT4AIGChip. === Logic synthesis and optimization === Logic synthesis starts from a high level hardware description and generates an optimized list of electronic gates, known as a gate level netlist, that is ready for placement, routing, and then construction in a specific manufacturing process. AI methods help with different parts of this process, including logic optimization, technology mapping, and making improvements after mapping. Supervised learning, especially with Graph Neural Networks (GNNs), is good at handling data or problems that can be represented as graphs. Since circuit diagrams are instances of directed graphs, supervised learning can help create models that predict design properties like power or error rates in circuits. In logic synthesis and optimization reinforcement learning is used to perform logic optimization directly. In some cases ag

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  • Research data archiving

    Research data archiving

    Research data archiving is the long-term storage of scholarly research data, including the natural sciences, social sciences, and life sciences. The various academic journals have differing policies regarding how much of their data and methods researchers are required to store in a public archive, and what is actually archived varies widely between different disciplines. Similarly, the major grant-giving institutions have varying attitudes towards public archiving of data. In general, the tradition of science has been for publications to contain sufficient information to allow fellow researchers to replicate and therefore test the research. In recent years this approach has become increasingly strained as research in some areas depends on large datasets which cannot easily be replicated independently. Data archiving is more important in some fields than others. In a few fields, all of the data necessary to replicate the work is already available in the journal article. In drug development, a great deal of data is generated and must be archived so researchers can verify that the reports the drug companies publish accurately reflect the data. Often used interchangeably, Data preservation and data archiving are both about protecting data for the long term, but they serve different purposes. Data preservation focuses on preventing data from being lost, damaged, or destroyed by creating backups, storing data in secure locations, and ensuring it remains accessible when needed. Data archiving, on the other hand, involves moving data that is no longer actively used to a separate storage location for long-term keeping. Archived data is often combined and compressed, and while it can still be accessed, it is not intended for regular use or frequent updates. The requirement of data archiving is a recent development in the history of science. It was made possible by advances in information technology allowing large amounts of data to be stored and accessed from central locations. For example, the American Geophysical Union (AGU) adopted their first policy on data archiving in 1993, about three years after the beginning of the WWW. This policy mandates that datasets cited in AGU papers must be archived by a recognised data center; it permits the creation of "data papers"; and it establishes AGU's role in maintaining data archives. But it makes no requirements on paper authors to archive their data. Prior to organized data archiving, researchers wanting to evaluate or replicate a paper would have to request data and methods information from the author. The academic community expects authors to share supplemental data. This process was recognized as wasteful of time and energy and obtained mixed results. Information could become lost or corrupted over the years. In some cases, authors simply refuse to provide the information. The need for data archiving and due diligence is greatly increased when the research deals with health issues or public policy formation. == Selected policies by journals == === Biotropica === Biotropica requires, as a condition for publication, that the data supporting the results in the paper and metadata describing them must be archived in an appropriate public archive such as Dryad, Figshare, GenBank, TreeBASE, or NCBI. Authors may elect to make the data publicly available as soon as the article is published or, if the technology of the archive allows, embargo access to the data up to three years after article publication. A statement describing Data Availability will be included in the manuscript as described in the instructions to authors. Exceptions to the required archiving of data may be granted at the discretion of the Editor-in-Chief for studies that include sensitive information (e.g., the location of endangered species). Our Editorial explaining the motivation for this policy can be found here. A more comprehensive list of data repositories is available here. Promoting a culture of collaboration with researchers who collect and archive data: The data collected by tropical biologists are often long-term, complex, and expensive to collect. The Board of Editors of Biotropica strongly encourages authors who re-use data archives archived data sets to include as fully engaged collaborators the scientists who originally collected them. We feel this will greatly enhance the quality and impact of the resulting research by drawing on the data collector’s profound insights into the natural history of the study system, reducing the risk of errors in novel analyses, and stimulating the cross-disciplinary and cross-cultural collaboration and training for which the ATBC and Biotropica are widely recognized. NB: Biotropica is one of only two journals that pays the fees for authors depositing data at Dryad. === The American Naturalist === The American Naturalist requires authors to deposit the data associated with accepted papers in a public archive. For gene sequence data and phylogenetic trees, deposition in GenBank or TreeBASE, respectively, is required. There are many possible archives that may suit a particular data set, including the Dryad repository for ecological and evolutionary biology data. All accession numbers for GenBank, TreeBASE, and Dryad must be included in accepted manuscripts before they go to Production. If the data is deposited somewhere else, please provide a link. If the data is culled from published literature, please deposit the collated data in Dryad for the convenience of your readers. Any impediments to data sharing should be brought to the attention of the editors at the time of submission so that appropriate arrangements can be worked out. === Journal of Heredity === The primary data underlying the conclusions of an article are critical to the verifiability and transparency of the scientific enterprise, and should be preserved in usable form for decades in the future. For this reason, Journal of Heredity requires that newly reported nucleotide or amino acid sequences, and structural coordinates, be submitted to appropriate public databases (e.g., GenBank; the EMBL Nucleotide Sequence Database; DNA Database of Japan; the Protein Data Bank; and Swiss-Prot). Accession numbers must be included in the final version of the manuscript. For other forms of data (e.g., microsatellite genotypes, linkage maps, images), the Journal endorses the principles of the Joint Data Archiving Policy (JDAP) in encouraging all authors to archive primary datasets in an appropriate public archive, such as Dryad, TreeBASE, or the Knowledge Network for Biocomplexity. Authors are encouraged to make data publicly available at time of publication or, if the technology of the archive allows, opt to embargo access to the data for a period up to a year after publication. The American Genetic Association also recognizes the vast investment of individual researchers in generating and curating large datasets. Consequently, we recommend that this investment be respected in secondary analyses or meta-analyses in a gracious collaborative spirit. === Molecular Ecology === Molecular Ecology expects that data supporting the results in the paper should be archived in an appropriate public archive, such as GenBank, Gene Expression Omnibus, TreeBASE, Dryad, the Knowledge Network for Biocomplexity, your own institutional or funder repository, or as Supporting Information on the Molecular Ecology web site. Data are important products of the scientific enterprise, and they should be preserved and usable for decades in the future. Authors may elect to have the data publicly available at time of publication, or, if the technology of the archive allows, may opt to embargo access to the data for a period up to a year after publication. Exceptions may be granted at the discretion of the editor, especially for sensitive information such as human subject data or the location of endangered species. === Nature === Such material must be hosted on an accredited independent site (URL and accession numbers to be provided by the author), or sent to the Nature journal at submission, either uploaded via the journal's online submission service, or if the files are too large or in an unsuitable format for this purpose, on CD/DVD (five copies). Such material cannot solely be hosted on an author's personal or institutional web site. Nature requires the reviewer to determine if all of the supplementary data and methods have been archived. The policy advises reviewers to consider several questions, including: "Should the authors be asked to provide supplementary methods or data to accompany the paper online? (Such data might include source code for modelling studies, detailed experimental protocols or mathematical derivations.) === Science === Science supports the efforts of databases that aggregate published data for the use of the scientific community. Therefore, before publication, large data sets (including microarray data, protein or DNA sequences, and atomic c

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  • Online analytical processing

    Online analytical processing

    In computing, online analytical processing (OLAP) (), is an approach to quickly answer multi-dimensional analytical (MDA) queries. The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture. OLAP tools enable users to analyse multidimensional data interactively from multiple perspectives. OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing. Consolidation involves the aggregation of data that can be accumulated and computed in one or more dimensions. For example, all sales offices are rolled up to the sales department or sales division to anticipate sales trends. By contrast, the drill-down is a technique that allows users to navigate through the details. For instance, users can view the sales by individual products that make up a region's sales. Slicing and dicing is a feature whereby users can take out (slicing) a specific set of data of the OLAP cube and view (dicing) the slices from different viewpoints. These viewpoints are sometimes called dimensions (such as looking at the same sales by salesperson, or by date, or by customer, or by product, or by region, etc.). Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad hoc queries with a rapid execution time. They borrow aspects of navigational databases, hierarchical databases and relational databases. OLAP is typically contrasted to OLTP (online transaction processing), which is generally characterized by much less complex queries, in a larger volume, to process transactions rather than for the purpose of business intelligence or reporting. Whereas OLAP systems are mostly optimized for read, OLTP has to process all kinds of queries (read, insert, update and delete). == Overview of OLAP systems == At the core of any OLAP system is an OLAP cube (also called a 'multidimensional cube' or a hypercube). It consists of numeric facts called measures that are categorized by dimensions. The measures are placed at the intersections of the hypercube, which is spanned by the dimensions as a vector space. The usual interface to manipulate an OLAP cube is a matrix interface, like Pivot tables in a spreadsheet program, which performs projection operations along the dimensions, such as aggregation or averaging. The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. Each measure can be thought of as having a set of labels, or meta-data associated with it. A dimension is what describes these labels; it provides information about the measure. A simple example would be a cube that contains a store's sales as a measure, and Date/Time as a dimension. Each Sale has a Date/Time label that describes more about that sale. For example: Sales Fact Table +-------------+----------+ | sale_amount | time_id | +-------------+----------+ Time Dimension | 930.10| 1234 |----+ +---------+-------------------+ +-------------+----------+ | | time_id | timestamp | | +---------+-------------------+ +---->| 1234 | 20080902 12:35:43 | +---------+-------------------+ === Multidimensional databases === Multidimensional structure is defined as "a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data". The structure is broken into cubes and the cubes are able to store and access data within the confines of each cube. "Each cell within a multidimensional structure contains aggregated data related to elements along each of its dimensions". Even when data is manipulated it remains easy to access and continues to constitute a compact database format. The data still remains interrelated. Multidimensional structure is quite popular for analytical databases that use online analytical processing (OLAP) applications. Analytical databases use these databases because of their ability to deliver answers to complex business queries swiftly. Data can be viewed from different angles, which gives a broader perspective of a problem unlike other models. === Aggregations === It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time required for the same query on OLTP relational data. The most important mechanism in OLAP which allows it to achieve such performance is the use of aggregations. Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions, using an aggregate function (or aggregation function). The number of possible aggregations is determined by every possible combination of dimension granularities. The combination of all possible aggregations and the base data contains the answers to every query which can be answered from the data. Because usually there are many aggregations that can be calculated, often only a predetermined number are fully calculated; the remainder are solved on demand. The problem of deciding which aggregations (views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time. View selection is NP-complete. Many approaches to the problem have been explored, including greedy algorithms, randomized search, genetic algorithms and A search algorithm. Some aggregation functions can be computed for the entire OLAP cube by precomputing values for each cell, and then computing the aggregation for a roll-up of cells by aggregating these aggregates, applying a divide and conquer algorithm to the multidimensional problem to compute them efficiently. For example, the overall sum of a roll-up is just the sum of the sub-sums in each cell. Functions that can be decomposed in this way are called decomposable aggregation functions, and include COUNT, MAX, MIN, and SUM, which can be computed for each cell and then directly aggregated; these are known as self-decomposable aggregation functions. In other cases, the aggregate function can be computed by computing auxiliary numbers for cells, aggregating these auxiliary numbers, and finally computing the overall number at the end; examples include AVERAGE (tracking sum and count, dividing at the end) and RANGE (tracking max and min, subtracting at the end). In other cases, the aggregate function cannot be computed without analyzing the entire set at once, though in some cases approximations can be computed; examples include DISTINCT COUNT, MEDIAN, and MODE; for example, the median of a set is not the median of medians of subsets. These latter are difficult to implement efficiently in OLAP, as they require computing the aggregate function on the base data, either computing them online (slow) or precomputing them for possible rollouts (large space). == Types == OLAP systems have been traditionally categorized using the following taxonomy. === Multidimensional OLAP (MOLAP) === MOLAP (multi-dimensional online analytical processing) is the classic form of OLAP and is sometimes referred to as just OLAP. MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database. Some MOLAP tools require the pre-computation and storage of derived data, such as consolidations – the operation known as processing. Such MOLAP tools generally utilize a pre-calculated data set referred to as a data cube. The data cube contains all the possible answers to a given range of questions. As a result, they have a very fast response to queries. On the other hand, updating can take a long time depending on the degree of pre-computation. Pre-computation can also lead to what is known as data explosion. Other MOLAP tools, particularly those that implement the functional database model do not pre-compute derived data but make all calculations on demand other than those that were previously requested and stored in a cache. Advantages of MOLAP Fast query performance due to optimized storage, multidimensional indexing and caching. Smaller on-disk size of data compared to data stored in relational database due to compression techniques. Automated computation of higher-level aggregates of the data. It is very compact for low dimension data se

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  • Accelerated Linear Algebra

    Accelerated Linear Algebra

    XLA (Accelerated Linear Algebra) is an open-source compiler for machine learning developed by the OpenXLA project. XLA is designed to improve the performance of machine learning models by optimizing the computation graphs at a lower level, making it particularly useful for large-scale computations and high-performance machine learning models. Key features of XLA include: Compilation of Computation Graphs: Compiles computation graphs into efficient machine code. Optimization Techniques: Applies operation fusion, memory optimization, and other techniques. Hardware Support: Optimizes models for various hardware, including CPUs, GPUs, and NPUs. Improved Model Execution Time: Aims to reduce machine learning models' execution time for both training and inference. Seamless Integration: Can be used with existing machine learning code with minimal changes. XLA represents a significant step in optimizing machine learning models, providing developers with tools to enhance computational efficiency and performance. == OpenXLA Project == OpenXLA Project is an open-source machine learning compiler and infrastructure initiative intended to provide a common set of tools for compiling and deploying machine learning models across different frameworks and hardware platforms. It provides a modular compilation stack that can be used by major deep learning frameworks like JAX, PyTorch, and TensorFlow. The project focuses on supplying shared components for optimization, portability, and execution across CPUs, GPUs, and specialized accelerators. Its design emphasizes interoperability between frameworks and a standardized set of representations for model computation. == Components == The OpenXLA ecosystem includes several core components: XLA – A deep learning compiler that optimizes computational graphs for multiple hardware targets. PJRT – A runtime interface that allows different back-ends to connect to XLA through a consistent API. StableHLO – A high-level operator set intended to serve as a stable, portable representation for ML models across compilers and frameworks. Shardy – An MLIR-based system for describing and transforming models that run in distributed or multi-device environments. Additional profiling, testing, and integration tools maintained under the OpenXLA organization. == Users and adopters == Several machine learning frameworks can use or interoperate with OpenXLA components, including JAX, TensorFlow, and parts of the PyTorch ecosystem. The project is developed with participation from multiple hardware and software organizations that contribute back-end integrations, testing, or specifications for their devices. This includes Alibaba, Amazon Web Services, AMD, Anyscale, Apple, Arm, Cerebras, Google, Graphcore, Hugging Face, Intel, Meta, NVIDIA and SiFive. == Supported target devices == x86-64 ARM64 NVIDIA GPU AMD GPU Intel GPU Apple GPU Google TPU AWS Trainium, Inferentia Cerebras Graphcore IPU == Governance == OpenXLA is developed as a community project with its work carried out in public repositories, discussion forums, and design meetings. Some components, such as StableHLO, began with stewardship from specific organizations and have outlined plans for more formal and distributed governance models as the project matures. == History == The project was announced in 2022 as an effort to coordinate development of ML compiler technologies across major AI companies, notably: Alibaba, Amazon Web Services, AMD, Anyscale, Apple, Arm, Cerebras, Google, Graphcore, Hugging Face, Intel, Meta, NVIDIA and SiFive.. It consolidated the XLA compiler, introduced StableHLO as a portable operator set, and created a unified structure for additional tools. Development continues within multiple repositories under the OpenXLA umbrella. It was founded by Eugene Burmako, James Rubin, Magnus Hyttsten, Mehdi Amini, Navid Khajouei, and Thea Lamkin from Google's Machine Learning organization.

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  • Artificial empathy

    Artificial empathy

    Artificial empathy or computational empathy is the development of AI systems—such as companion robots or virtual agents—that can detect emotions and respond to them in an empathic way. Although such technology can be perceived as scary or threatening, it could also have a significant advantage over humans for roles in which emotional expression can be important, such as in the health care sector. An October 2025 review and meta-analysis in the British Medical Bulletin found that AI chatbots were rated as showing more empathy than human healthcare professionals in 13 of 15 studies that compared them. Care-givers who perform emotional labor above and beyond the requirements of paid labor can experience chronic stress or burnout, and can become desensitized to patients. Artificial empathy could also help the socialization of care-givers, or serve as role model for emotional detachment. A broader definition of artificial empathy is "the ability of nonhuman models to predict a person's internal state (e.g., cognitive, affective, physical) given the signals (s)he emits (e.g., facial expression, voice, gesture) or to predict a person's reaction (including, but not limited to internal states) when he or she is exposed to a given set of stimuli (e.g., facial expression, voice, gesture, graphics, music, etc.)". A 2025 study reported that some multimodal large language models can recognize basic facial expressions with human-level accuracy on a commonly used research dataset of posed facial expressions. == Areas of research == There are a variety of philosophical, theoretical, and applicative questions related to artificial empathy. For example: Which conditions would have to be met for a robot to respond competently to a human emotion? What models of empathy can or should be applied to Social and Assistive Robotics? Must the interaction of humans with robots imitate affective interaction between humans? Can a robot help science learn about affective development of humans? Would robots create unforeseen categories of inauthentic relations? What relations with robots can be considered authentic? How can we assess artificial empathy in AI systems? == Examples of artificial empathy research and practice == People often communicate and make decisions based on inferences about each other's internal states (e.g., emotional, cognitive, and physical states) that are in turn based on signals emitted by the person such as facial expression, body gesture, voice, and words. Broadly speaking, artificial empathy focuses on developing non-human models that achieve similar objectives using similar data. === Streams of artificial empathy research === Artificial empathy has been applied in various research disciplines, including artificial intelligence and business. Two main streams of research in this domain are: the use of nonhuman models to predict a person's internal state (e.g., cognitive, affective, physical) given the signals he or she emits (e.g., facial expression, voice, gesture) the use of nonhuman models to predict a person's reaction when he or she is exposed to a given set of stimuli (e.g., facial expression, voice, gesture, graphics, music, etc.). Research on affective computing, such as emotional speech recognition and facial expression detection, falls within the first stream of artificial empathy. Contexts that have been studied include oral interviews, call centers, human-computer interaction, sales pitches, and financial reporting. The second stream of artificial empathy has been researched more in marketing contexts, such as advertising, branding, customer reviews, in-store recommendation systems, movies, and online dating. === Artificial empathy applications in practice === With the increasing volume of visual, audio, and text data in commerce, many business applications for artificial empathy have followed. For example, Affectiva analyses viewers' facial expressions from video recordings while they are watching video advertisements in order to optimize the content design of video ads. Software like HireVue, BarRaiser, a hiring intelligence firm, helps firms make recruitment decisions by analyzing audio and video information from candidates' video interviews. Lapetus Solutions develops a model to estimate an individual's longevity, health status, and disease susceptibility from a face photo. Their technology has been applied in the insurance industry. == Artificial empathy and human services == Although artificial intelligence cannot yet replace social workers themselves, the technology has been deployed in that field. Florida State University published a study about Artificial Intelligence being used in the human services field. The research used computer algorithms to analyze health records for combinations of risk factors that could predict a future suicide attempt. The article reports, "machine learning—a future frontier for artificial intelligence—can predict with 80% to 90% accuracy whether someone will attempt suicide as far off as two years into the future. The algorithms become even more accurate as a person's suicide attempt gets closer. For example, the accuracy climbs to 92% one week before a suicide attempt when artificial intelligence focuses on general hospital patients". Such algorithmic machines can help social workers. Social work operates on a cycle of engagement, assessment, intervention, and evaluation with clients. Earlier assessment for risk of suicide can lead to earlier interventions and prevention, therefore saving lives. The system would learn, analyze, and detect risk factors, alerting the clinician of a patient's suicide risk score (analogous to a patient's cardiovascular risk score). Then, social workers could step in for further assessment and preventive intervention.

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  • Metadata management

    Metadata management

    Metadata management involves managing metadata about other data, whereby this "other data" is generally referred to as content data. The term is used most often in relation to digital media, but older forms of metadata are catalogs, dictionaries, and taxonomies. For example, the Dewey Decimal Classification is a metadata management system developed in 1876 for libraries. == Metadata schema == Metadata management goes by the end-to-end process and governance framework for creating, controlling, enhancing, attributing, defining and managing a metadata schema, model or other structured aggregation system, either independently or within a repository and the associated supporting processes (often to enable the management of content). For web-based systems, URLs, images, video etc. may be referenced from a triples table of object, attribute and value. == Scope == With specific knowledge domains, the boundaries of the metadata for each must be managed, since a general ontology is not useful to experts in one field whose language is knowledge-domain specific. == Metadata Manager == In the process of developing a knowledge management solution, creating a metadata schema, and a system in which metadata is managed, a dedicated resource may be appointed to maintain adherence to metadata standards as defined by data owners as well as general best practice. This person is responsible for curation of the business and technical layers of the metadata schema, and commonly involved with strategy and implementation. A metadata manager is not required to master all aspects, or be involved with everything concerning the solution, but an understanding of as much of the process as possible to ensure a relevant schema is developed. == Metadata management over time == Managing the metadata in a knowledge management solution is an important step in a metadata strategy. It is part of the strategy to make sure that the metadata are complete, current and correct at any given time. Managing a metadata project is also about making sure that users of the system are aware of the possibilities allowed by a well-designed metadata system and how to maximize the benefits of metadata. Regularly monitoring the metadata to ensure that the schema remains relevant is advised. === Wikipedia metadata === Wikipedia is a project that actively manages metadata for its articles and files. For example, volunteer editors carefully curate new biographical articles based on the notability (claim to fame), name, birth, and/or death dates. Similarly, volunteer editors carefully curate new architectural articles based on name, municipality, or geo coordinates. When new articles with a valid alternate spelling are added to Wikipedia that match up to existing articles based on metadata, these are then manually checked and if needed, tagged for merging. When new articles are added that are considered out of scope or otherwise unfit for Wikipedia, these are nominated for deletion. To help keep track of metadata on Wikipedia, the new Wikimedia project Wikidata was established in 2012. Click on the pictures to view more metadata about these images:

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