AI Art Legality

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Legal information retrieval

Legal information retrieval is the science of information retrieval applied to legal text, including legislation, case law, and scholarly works. Accurate legal information retrieval is important to provide access to the law to laymen and legal professionals. Its importance has increased because of the vast and quickly increasing amount of legal documents available through electronic means. Legal information retrieval is a part of the growing field of legal informatics. In a legal setting, it is frequently important to retrieve all information related to a specific query. However, commonly used boolean search methods (exact matches of specified terms) on full text legal documents have been shown to have an average recall rate as low as 20 percent, meaning that only 1 in 5 relevant documents are actually retrieved. In that case, researchers believed that they had retrieved over 75% of relevant documents. This may result in failing to retrieve important or precedential cases. In some jurisdictions this may be especially problematic, as legal professionals are ethically obligated to be reasonably informed as to relevant legal documents. Legal Information Retrieval attempts to increase the effectiveness of legal searches by increasing the number of relevant documents (providing a high recall rate) and reducing the number of irrelevant documents (a high precision rate). This is a difficult task, as the legal field is prone to jargon, polysemes (words that have different meanings when used in a legal context), and constant change. Techniques used to achieve these goals generally fall into three categories: boolean retrieval, manual classification of legal text, and natural language processing of legal text. == Problems == Application of standard information retrieval techniques to legal text can be more difficult than application in other subjects. One key problem is that the law rarely has an inherent taxonomy. Instead, the law is generally filled with open-ended terms, which may change over time. This can be especially true in common law countries, where each decided case can subtly change the meaning of a certain word or phrase. Legal information systems must also be programmed to deal with law-specific words and phrases. Though this is less problematic in the context of words which exist solely in law, legal texts also frequently use polysemes, words may have different meanings when used in a legal or common-speech manner, potentially both within the same document. The legal meanings may be dependent on the area of law in which it is applied. For example, in the context of European Union legislation, the term "worker" has four different meanings: Any worker as defined in Article 3(a) of Directive 89/391/EEC who habitually uses display screen equipment as a significant part of his normal work. Any person employed by an employer, including trainees and apprentices but excluding domestic servants; Any person carrying out an occupation on board a vessel, including trainees and apprentices, but excluding port pilots and shore personnel carrying out work on board a vessel at the quayside; Any person who, in the Member State concerned, is protected as an employee under national employment law and in accordance with national practice; It also has the common meaning: A person who works at a specific occupation. Though the terms may be similar, correct information retrieval must differentiate between the intended use and irrelevant uses in order to return the correct results. Even if a system overcomes the language problems inherent in law, it must still determine the relevancy of each result. In the context of judicial decisions, this requires determining the precedential value of the case. Case decisions from senior or superior courts may be more relevant than those from lower courts, even where the lower court's decision contains more discussion of the relevant facts. The opposite may be true, however, if the senior court has only a minor discussion of the topic (for example, if it is a secondary consideration in the case). An information retrieval system must also be aware of the authority of the jurisdiction. A case from a binding authority is most likely of more value than one from a non-binding authority. Additionally, the intentions of the user may determine which cases they find valuable. For instance, where a legal professional is attempting to argue a specific interpretation of law, he might find a minor court's decision which supports his position more valuable than a senior courts position which does not. He may also value similar positions from different areas of law, different jurisdictions, or dissenting opinions. Overcoming these problems can be made more difficult because of the large number of cases available. The number of legal cases available via electronic means is constantly increasing (in 2003, US appellate courts handed down approximately 500 new cases per day), meaning that an accurate legal information retrieval system must incorporate methods of both sorting past data and managing new data. == Techniques == === Boolean searches === Boolean searches, where a user may specify terms such as use of specific words or judgments by a specific court, are the most common type of search available via legal information retrieval systems. They are widely implemented but overcome few of the problems discussed above. The recall and precision rates of these searches vary depending on the implementation and searches analyzed. One study found a basic boolean search's recall rate to be roughly 20%, and its precision rate to be roughly 79%. Another study implemented a generic search (that is, not designed for legal uses) and found a recall rate of 56% and a precision rate of 72% among legal professionals. Both numbers increased when searches were run by non-legal professionals, to a 68% recall rate and 77% precision rate. This is likely explained because of the use of complex legal terms by the legal professionals. === Manual classification === In order to overcome the limits of basic boolean searches, information systems have attempted to classify case laws and statutes into more computer friendly structures. Usually, this results in the creation of an ontology to classify the texts, based on the way a legal professional might think about them. These attempt to link texts on the basis of their type, their value, and/or their topic areas. Most major legal search providers now implement some sort of classification search, such as Westlaw's “Natural Language” or LexisNexis' Headnote searches. Additionally, both of these services allow browsing of their classifications, via Westlaw's West Key Numbers or Lexis' Headnotes. Though these two search algorithms are proprietary and secret, it is known that they employ manual classification of text (though this may be computer-assisted). These systems can help overcome the majority of problems inherent in legal information retrieval systems, in that manual classification has the greatest chances of identifying landmark cases and understanding the issues that arise in the text. In one study, ontological searching resulted in a precision rate of 82% and a recall rate of 97% among legal professionals. The legal texts included, however, were carefully controlled to just a few areas of law in a specific jurisdiction. The major drawback to this approach is the requirement of using highly skilled legal professionals and large amounts of time to classify texts. As the amount of text available continues to increase, some have stated their belief that manual classification is unsustainable. === Natural language processing === In order to reduce the reliance on legal professionals and the amount of time needed, efforts have been made to create a system to automatically classify legal text and queries. Adequate translation of both would allow accurate information retrieval without the high cost of human classification. These automatic systems generally employ Natural Language Processing (NLP) techniques that are adapted to the legal domain, and also require the creation of a legal ontology. Though multiple systems have been postulated, few have reported results. One system, “SMILE,” which attempted to automatically extract classifications from case texts, resulted in an f-measure (which is a calculation of both recall rate and precision) of under 0.3 (compared to perfect f-measure of 1.0). This is probably much lower than an acceptable rate for general usage. Despite the limited results, many theorists predict that the evolution of such systems will eventually replace manual classification systems. === Citation-Based ranking === In the mid-90s the Room 5 case law retrieval project used citation mining for summaries and ranked its search results based on citation type and count. This slightly pre-dated the PageRank algorithm at Stanford which was also a citation-based ranking. Ranking of results was based
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Poop Map

Poop Map is a social app where users can track on a map where and when they defecate. In addition to logging location and time of each bowel movement, users can also add a photo, "like" other users' logs, and rate each account. The social elements of the app allow for groups of users to create a competitive league. Certain behaviors unlock achievements in-app. == Development == The app was created by app developer Nino Uzelac. It was launched in July 2013. == Popularity == The app charted at number one on the Apple App Store charts in 2021 after going viral on TikTok. As of September 2024, the app has a 4.8 rating on the App Store and more than 58,000 ratings. It also has more than one million downloads on the Google Play Store. Poop Map is notably popular among hikers, and has been written about in the outdoors magazine Outside.
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Color space

A color space is a specific organization of colors. In combination with color profiling supported by various physical devices, it supports reproducible representations of color – whether such representation entails an analog or a digital representation. A color space may be arbitrary, i.e. with physically realized colors assigned to a set of physical color swatches with corresponding assigned color names (including discrete numbers in – for example – the Pantone collection), or structured with mathematical rigor (as with the NCS System, Adobe RGB and sRGB). A "color space" is a useful conceptual tool for understanding the color capabilities of a particular device or digital file. When trying to reproduce color on another device, color spaces can show whether shadow/highlight detail and color saturation can be retained, and by how much either will be compromised. A "color model" is an abstract mathematical model describing the way colors can be represented as tuples of numbers (e.g. triples in RGB or quadruples in CMYK); however, a color model with no associated mapping function to an absolute color space is a more or less arbitrary color system with no connection to any globally understood system of color interpretation. Adding a specific mapping function between a color model and a reference color space establishes within the reference color space a definite "footprint", known as a gamut, and for a given color model, this defines a color space. For example, Adobe RGB and sRGB are two different absolute color spaces, both based on the RGB color model. When defining a color space, the usual reference standard is the CIELAB or CIEXYZ color spaces, which were specifically designed to encompass all colors the average human can see. Since "color space" identifies a particular combination of the color model and the mapping function, the word is often used informally to identify a color model. However, even though identifying a color space automatically identifies the associated color model, this usage is incorrect in a strict sense. For example, although several specific color spaces are based on the RGB color model, there is no such thing as the singular RGB color space. == History == In 1802, Thomas Young postulated the existence of three types of photoreceptors (now known as cone cells) in the eye, each of which was sensitive to a particular range of visible light. Hermann von Helmholtz developed the Young–Helmholtz theory further in 1850: that the three types of cone photoreceptors could be classified as short-preferring (blue), middle-preferring (green), and long-preferring (red), according to their response to the wavelengths of light striking the retina. The relative strengths of the signals detected by the three types of cones are interpreted by the brain as a visible color. But it is not clear that they thought of colors as being points in color space. The color-space concept was likely due to Hermann Grassmann, who developed it in two stages. First, he developed the idea of vector space, which allowed the algebraic representation of geometric concepts in n-dimensional space. Fearnley-Sander (1979) describes Grassmann's foundation of linear algebra as follows: The definition of a linear space (vector space)... became widely known around 1920, when Hermann Weyl and others published formal definitions. In fact, such a definition had been given thirty years previously by Peano, who was thoroughly acquainted with Grassmann's mathematical work. Grassmann did not put down a formal definition—the language was not available—but there is no doubt that he had the concept. With this conceptual background, in 1853, Grassmann published a theory of how colors mix; it and its three color laws are still taught, as Grassmann's law. As noted first by Grassmann... the light set has the structure of a cone in the infinite-dimensional linear space. As a result, a quotient set (with respect to metamerism) of the light cone inherits the conical structure, which allows color to be represented as a convex cone in the 3- D linear space, which is referred to as the color cone. == Examples == Colors can be created in printing with color spaces based on the CMYK color model, using the subtractive primary colors of pigment (cyan, magenta, yellow, and key [black]). To create a three-dimensional representation of a given color space, we can assign the amount of magenta color to the representation's X axis, the amount of cyan to its Y axis, and the amount of yellow to its Z axis. The resulting 3-D space provides a unique position for every possible color that can be created by combining those three pigments. Colors can be created on computer monitors with color spaces based on the RGB color model, using the additive primary colors (red, green, and blue). A three-dimensional representation would assign each of the three colors to the X, Y, and Z axes. Colors generated on a given monitor will be limited by the reproduction medium, such as the phosphor (in a CRT monitor) or filters and backlight (LCD monitor). Another way of creating colors on a monitor is with an HSL or HSV color model, based on hue, saturation, brightness (value/lightness). With such a model, the variables are assigned to cylindrical coordinates. Many color spaces can be represented as three-dimensional values in this manner, but some have more, or fewer dimensions, and some, such as Pantone, cannot be represented in this way at all. == Conversion == Color space conversion is the translation of the representation of a color from one basis to another. This typically occurs in the context of converting an image that is represented in one color space to another color space, the goal being to make the translated image look as similar as possible to the original. == RGB density == The RGB color model is implemented in different ways, depending on the capabilities of the system used. The most common incarnation in general use as of 2021 is the 24-bit implementation, with 8 bits, or 256 discrete levels of color per channel. Any color space based on such a 24-bit RGB model is thus limited to a range of 256×256×256 ≈ 16.7 million colors. Some implementations use 16 bits per component for 48 bits total, resulting in the same gamut with a larger number of distinct colors. This is especially important when working with wide-gamut color spaces (where most of the more common colors are located relatively close together), or when a large number of digital filtering algorithms are used consecutively. The same principle applies for any color space based on the same color model, but implemented at different bit depths. == Lists == CIE 1931 XYZ color space was one of the first attempts to produce a color space based on measurements of human color perception (earlier efforts were by James Clerk Maxwell, König & Dieterici, and Abney at Imperial College) and it is the basis for almost all other color spaces. The CIERGB color space is a linearly-related companion of CIE XYZ. Additional derivatives of CIE XYZ include the CIELUV, CIEUVW, and CIELAB. === Generic === RGB uses additive color mixing, because it describes what kind of light needs to be emitted to produce a given color. RGB stores individual values for red, green and blue. RGBA is RGB with an additional channel, alpha, to indicate transparency. Common color spaces based on the RGB model include sRGB, Adobe RGB, ProPhoto RGB, scRGB, and CIE RGB. CMYK uses subtractive color mixing used in the printing process, because it describes what kind of inks need to be applied so the light reflected from the substrate and through the inks produces a given color. One starts with a white substrate (canvas, page, etc.), and uses ink to subtract color from white to create an image. CMYK stores ink values for cyan, magenta, yellow and black. There are many CMYK color spaces for different sets of inks, substrates, and press characteristics (which change the dot gain or transfer function for each ink and thus change the appearance). YIQ was formerly used in NTSC (North America, Japan and elsewhere) television broadcasts for historical reasons. This system stores a luma value roughly analogous to (and sometimes incorrectly identified as) luminance, along with two chroma values as approximate representations of the relative amounts of blue and red in the color. It is similar to the YUV scheme used in most video capture systems and in PAL (Australia, Europe, except France, which uses SECAM) television, except that the YIQ color space is rotated 33° with respect to the YUV color space and the color axes are swapped. The YDbDr scheme used by SECAM television is rotated in another way. YPbPr is a scaled version of YUV. It is most commonly seen in its digital form, YCbCr, used widely in video and image compression schemes such as MPEG and JPEG. xvYCC is an international digital video color space standard published by the IEC (IEC 61966-2-4). It is based on the ITU BT.601 and BT.709
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GNU toolchain

The GNU toolchain is a broad collection of programming tools produced by the GNU Project. These tools form a toolchain (a suite of tools used in a serial manner) used for developing software applications and operating systems. The GNU toolchain plays a vital role in development of Linux, some BSD systems, and software for embedded systems. Parts of the GNU toolchain are also directly used with or ported to other platforms such as Solaris, macOS, Microsoft Windows (via Cygwin and MinGW/MSYS/WSL2), Sony PlayStation Portable (used by PSP modding scene) and Sony PlayStation 3. == Components == Projects in the GNU toolchain are: GNU Autotools (build system) – Software build toolset from GNU GNU Binutils – GNU software development tools for executable code GNU Bison – Yacc-compatible parser generator program GNU C Library – GNU implementation of the standard C libraryPages displaying short descriptions of redirect targets GNU Compiler Collection – Free and open-source compiler for various programming languages GNU Debugger – Source-level debugger GNU m4 – General-purpose macro processor GNU make – Software build automation tool
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CloudHealth Technologies

CloudHealth Technologies, now CloudHealth by VMware, is a software company based in Boston, Massachusetts. The company provides cloud computing services related to cost management, governance, automation, security, and performance. == History == CloudHealth Technologies was founded by Joe Kinsella in 2012. Dan Phillips joined as CEO and co-founder in late 2012, and Dave Eicher joined as co-Founder in January 2013. In May 2016, the company announced plans to expand from its Boston headquarters with branch offices in San Francisco, London, Washington, D.C., Sydney, Amsterdam, Tel Aviv, and Singapore. Headquarters moved in Boston from Fort Point to 100 Summer Street in the Spring of 2018, tripling in square footage. In September 2017, Tom Axbey—who was previously at Rave Mobile Safety—joined as the new CEO and President. VMware announced its intention to acquire CloudHealth Technologies on August 27, 2018. The acquisition is "part of the information technology company's continued push into cloud-based software services" according to Reuters. The deal closed on October 4, 2018, and was reported to be in excess of $500 million. == Technology == Delivered through a software as a service (SaaS) model, CloudHealth Technologies's platform collects and analyzes data from cloud computing services and other IT environments so clients can report on costs, inform their business models, and project future trends. CloudHealth Technologies is compatible with Amazon Web Services, Microsoft Azure, Google Cloud Platform, multicloud, and hybrid cloud environments. CloudHealth Technologies has received Amazon Web Services(AWS) Education Competency status, AWS Migration Competency status and achieved SOC 2 Type 2 Compliance. == Funding == As of June 2017, CloudHealth Technologies has raised a total of $85.7 million through four rounds of funding. In March 2013, CloudHealth Technologies announced that it had secured $4.5 million in Series A funding. This round was led by .406 Ventures and Sigma Prime Ventures. In January 2015, CloudHealth Technologies secured $12 million in Series B funding. This round was led by Scale Venture Partners, .406 Ventures, and Sigma Prime Ventures, and was followed by a $3.2 million extension round. In May 2016, CloudHealth Technologies announced $20 million in Series C funding, led by Sapphire Ventures, .406 Ventures, Scale Venture Partners and Sigma Prime Ventures. In June 2017, CloudHealth Technologies secured $46 million in Series D funding led by Kleiner Perkins Caufield & Byers with participation from Meritech Capital Partners, Sapphire Ventures, 406 Ventures, and Scale Venture Partners. == Competition == As of March 2023, CloudHealth Technologies competes with Cloudability by Apptio and CloudCheckr by NetApp.
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Cloud robotics

Cloud robotics is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centered on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of a modern data center in the cloud, which can process and share information from various robots or agents (other machines, smart objects, humans, etc.). Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be gain capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low-cost, smarter robots with an intelligent "brain" in the cloud. The "brain" consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc. == Components == A cloud for robots potentially has at least six significant components: Building a "cloud brain" for robots, the main object of cloud robotics; Offering a global library of images, maps, and object data, often with geometry and mechanical properties, expert system, knowledge base (i.e. semantic web, data centres); Massively-parallel computation on demand for sample-based statistical modelling and motion planning, task planning, multi-robot collaboration, scheduling and coordination of system; Robot sharing of outcomes, trajectories, and dynamic control policies and robot learning support; Human sharing of open-source code, data, and designs for programming, experimentation, and hardware construction; On-demand human guidance and assistance for evaluation, learning, and error recovery; Augmented human–robot interaction through various ways (semantics knowledge base, Apple SIRI like service, etc.). == Applications == Autonomous mobile robots Google's self-driving cars are cloud robots. The cars use the network to access Google's enormous database of maps and satellite and environment model (like Streetview) and combines it with streaming data from GPS, cameras, and 3D sensors to monitor its own position within centimetres, and with past and current traffic patterns to avoid collisions. Each car can learn something about environments, roads, or driving, or conditions, and it sends the information to the Google cloud, where it can be used to improve the performance of other cars. Cloud medical robots a medical cloud (also called a healthcare cluster) consists of various services such as a disease archive, electronic medical records, a patient health management system, practice services, analytics services, clinic solutions, expert systems, etc. A robot can connect to the cloud to provide clinical service to patients, as well as deliver assistance to doctors (e.g. a co-surgery robot). Moreover, it also provides a collaboration service by sharing information between doctors and care givers about clinical treatment. Assistive robots A domestic robot can be employed for healthcare and life monitoring for elderly people. The system collects the health status of users and exchange information with cloud expert system or doctors to facilitate elderly peoples life, especially for those with chronic diseases. For example, the robots are able to provide support to prevent the elderly from falling down, emergency healthy support such as heart disease, blooding disease. Care givers of elderly people can also get notification when in emergency from the robot through network. Industrial robots As highlighted by the German government's Industry 4.0 Plan, "Industry is on the threshold of the fourth industrial revolution. Driven by the Internet, the real and virtual worlds are growing closer and closer together to form the Internet of Things. Industrial production of the future will be characterised by the strong individualisation of products under the conditions of highly flexible (large series) production, the extensive integration of customers and business partners in business and value-added processes, and the linking of production and high-quality services leading to so-called hybrid products." In manufacturing, such cloud based robot systems could learn to handle tasks such as threading wires or cables, or aligning gaskets from a professional knowledge base. A group of robots can share information for some collaborative tasks. Even more, a consumer is able to place customised product orders to manufacturing robots directly with online ordering systems. Another potential paradigm is shopping-delivery robot systems. Once an order is placed, a warehouse robot dispatches the item to an autonomous car or autonomous drone to deliver it to its recipient. == Research == RoboEarth was funded by the European Union's Seventh Framework Programme for research, technological development projects, specifically to explore the field of cloud robotics. The goal of RoboEarth is to allow robotic systems to benefit from the experience of other robots, paving the way for rapid advances in machine cognition and behaviour, and ultimately, for more subtle and sophisticated human-machine interaction. RoboEarth offers a Cloud Robotics infrastructure. RoboEarth's World-Wide-Web style database stores knowledge generated by humans – and robots – in a machine-readable format. Data stored in the RoboEarth knowledge base include software components, maps for navigation (e.g., object locations, world models), task knowledge (e.g., action recipes, manipulation strategies), and object recognition models (e.g., images, object models). The RoboEarth Cloud Engine includes support for mobile robots, autonomous vehicles, and drones, which require much computation for navigation. Rapyuta is an open source cloud robotics framework based on RoboEarth Engine developed by the robotics researcher at ETHZ. Within the framework, each robot connected to Rapyuta can have a secured computing environment (rectangular boxes) giving them the ability to move their heavy computation into the cloud. In addition, the computing environments are tightly interconnected with each other and have a high bandwidth connection to the RoboEarth knowledge repository. FogROS2 is an open-source extension to the Robot Operating System 2 (ROS 2) developed by researchers at UC Berkeley. It enables robots to offload computationally intensive tasks—such as SLAM, grasp planning, and motion planning—to cloud resources, thereby enhancing performance and reducing onboard computational requirements. FogROS2 automates the provisioning of cloud instances, deployment of ROS 2 nodes, and secure communication between robots and cloud services. The platform is designed to be compatible with existing ROS 2 applications without requiring code modifications. Further advancements include FogROS2-SGC, which facilitates secure global connectivity across different networks and locations, and FogROS2-FT, which introduces fault tolerance by replicating services across multiple cloud providers to ensure robustness against failures. KnowRob is an extensional project of RoboEarth. It is a knowledge processing system that combines knowledge representation and reasoning methods with techniques for acquiring knowledge and for grounding the knowledge in a physical system and can serve as a common semantic framework for integrating information from different sources. RoboBrain is a large-scale computational system that learns from publicly available Internet resources, computer simulations, and real-life robot trials. It accumulates everything robotics into a comprehensive and interconnected knowledge base. Applications include prototyping for robotics research, household robots, and self-driving cars. The goal is as direct as the project's name—to create a centralised, always-online brain for robots to tap into. The project is dominated by Stanford University and Cornell University. And the project is supported by the National Science Foundation, the Office of Naval Research, the Army Research Office, Google, Microsoft, Qualcomm, the Alfred P. Sloan Foundation and the National Robotics Initiative, whose goal is to advance robotics to help make the United States more competitive in the world economy. MyRobots is a service for connecting robots and intelligent devices to the Internet. It can be regarded as a social network for robots and smart objects (i.e. Facebook for robots). With socialising, collaborating and sharing, robots can benefit from those interactions too by sharing their sensor information giving insight on their perspective of their current state. COALAS is funded by the INTERREG IVA France (Channel) – England European cross-border co-operation programme. The project aims to develop new technologies for disabled people through social and technological innovation and through the users' social and psychological integrity. The objective is to produce a cognitive ambient
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View model

A view model or viewpoints framework in systems engineering, software engineering, and enterprise engineering is a framework which defines a coherent set of views to be used in the construction of a system architecture, software architecture, or enterprise architecture. A view is a representation of the whole system from the perspective of a related set of concerns. Since the early 1990s there have been a number of efforts to prescribe approaches for describing and analyzing system architectures. A result of these efforts have been to define a set of views (or viewpoints). They are sometimes referred to as architecture frameworks or enterprise architecture frameworks, but are usually called "view models". Usually a view is a work product that presents specific architecture data for a given system. However, the same term is sometimes used to refer to a view definition, including the particular viewpoint and the corresponding guidance that defines each concrete view. The term view model is related to view definitions. == Overview == The purpose of views and viewpoints is to enable humans to comprehend very complex systems, to organize the elements of the problem and the solution around domains of expertise and to separate concerns. In the engineering of physically intensive systems, viewpoints often correspond to capabilities and responsibilities within the engineering organization. Most complex system specifications are so extensive that no single individual can fully comprehend all aspects of the specifications. Furthermore, we all have different interests in a given system and different reasons for examining the system's specifications. A business executive will ask different questions of a system make-up than would a system implementer. The concept of viewpoints framework, therefore, is to provide separate viewpoints into the specification of a given complex system in order to facilitate communication with the stakeholders. Each viewpoint satisfies an audience with interest in a particular set of aspects of the system. Each viewpoint may use a specific viewpoint language that optimizes the vocabulary and presentation for the audience of that viewpoint. Viewpoint modeling has become an effective approach for dealing with the inherent complexity of large distributed systems. Architecture description practices, as described in IEEE Std 1471-2000, utilize multiple views to address several areas of concerns, each one focusing on a specific aspect of the system. Examples of architecture frameworks using multiple views include Kruchten's "4+1" view model, the Zachman Framework, TOGAF, DoDAF, and RM-ODP. == History == In the 1970s, methods began to appear in software engineering for modeling with multiple views. Douglas T. Ross and K.E. Schoman in 1977 introduce the constructs context, viewpoint, and vantage point to organize the modeling process in systems requirements definition. According to Ross and Schoman, a viewpoint "makes clear what aspects are considered relevant to achieving ... the overall purpose [of the model]" and determines How do we look at [a subject being modelled]? As examples of viewpoints, the paper offers: Technical, Operational and Economic viewpoints. In 1992, Anthony Finkelstein and others published a very important paper on viewpoints. In that work: "A viewpoint can be thought of as a combination of the idea of an “actor”, “knowledge source”, “role” or “agent” in the development process and the idea of a “view” or “perspective” which an actor maintains." An important idea in this paper was to distinguish "a representation style, the scheme and notation by which the viewpoint expresses what it can see" and "a specification, the statements expressed in the viewpoint's style describing particular domains". Subsequent work, such as IEEE 1471, preserved this distinction by utilizing two separate terms: viewpoint and view, respectively. Since the early 1990s there have been a number of efforts to codify approaches for describing and analyzing system architectures. These are often termed architecture frameworks or sometimes viewpoint sets. Many of these have been funded by the United States Department of Defense, but some have sprung from international or national efforts in ISO or the IEEE. Among these, the IEEE Recommended Practice for Architectural Description of Software-Intensive Systems (IEEE Std 1471-2000) established useful definitions of view, viewpoint, stakeholder and concern and guidelines for documenting a system architecture through the use of multiple views by applying viewpoints to address stakeholder concerns. The advantage of multiple views is that hidden requirements and stakeholder disagreements can be discovered more readily. However, studies show that in practice, the added complexity of reconciling multiple views can undermine this advantage. IEEE 1471 (now ISO/IEC/IEEE 42010:2011, Systems and software engineering — Architecture description) prescribes the contents of architecture descriptions and describes their creation and use under a number of scenarios, including precedented and unprecedented design, evolutionary design, and capture of design of existing systems. In all of these scenarios the overall process is the same: identify stakeholders, elicit concerns, identify a set of viewpoints to be used, and then apply these viewpoint specifications to develop the set of views relevant to the system of interest. Rather than define a particular set of viewpoints, the standard provides uniform mechanisms and requirements for architects and organizations to define their own viewpoints. In 1996 the ISO Reference Model for Open Distributed Processing (RM-ODP) was published to provide a useful framework for describing the architecture and design of large-scale distributed systems. == View model topics == === View === A view of a system is a representation of the system from the perspective of a viewpoint. This viewpoint on a system involves a perspective focusing on specific concerns regarding the system, which suppresses details to provide a simplified model having only those elements related to the concerns of the viewpoint. For example, a security viewpoint focuses on security concerns and a security viewpoint model contains those elements that are related to security from a more general model of a system. A view allows a user to examine a portion of a particular interest area. For example, an Information View may present all functions, organizations, technology, etc. that use a particular piece of information, while the Organizational View may present all functions, technology, and information of concern to a particular organization. In the Zachman Framework views comprise a group of work products whose development requires a particular analytical and technical expertise because they focus on either the “what,” “how,” “who,” “where,” “when,” or “why” of the enterprise. For example, Functional View work products answer the question “how is the mission carried out?” They are most easily developed by experts in functional decomposition using process and activity modeling. They show the enterprise from the point of view of functions. They also may show organizational and information components, but only as they relate to functions. === Viewpoints === In systems engineering, a viewpoint is a partitioning or restriction of concerns in a system. Adoption of a viewpoint is usable so that issues in those aspects can be addressed separately. A good selection of viewpoints also partitions the design of the system into specific areas of expertise. Viewpoints provide the conventions, rules, and languages for constructing, presenting and analysing views. In ISO/IEC 42010:2007 (IEEE-Std-1471-2000) a viewpoint is a specification for an individual view. A view is a representation of a whole system from the perspective of a viewpoint. A view may consist of one or more architectural models. Each such architectural model is developed using the methods established by its associated architectural system, as well as for the system as a whole. === Modeling perspectives === Modeling perspectives is a set of different ways to represent pre-selected aspects of a system. Each perspective has a different focus, conceptualization, dedication and visualization of what the model is representing. In information systems, the traditional way to divide modeling perspectives is to distinguish the structural, functional and behavioral/processual perspectives. This together with rule, object, communication and actor and role perspectives is one way of classifying modeling approaches === Viewpoint model === In any given viewpoint, it is possible to make a model of the system that contains only the objects that are visible from that viewpoint, but also captures all of the objects, relationships and constraints that are present in the system and relevant to that viewpoint. Such a model is said to be a viewpoint model, or a view of the
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Cloud-computing comparison

The following is a comparison of cloud-computing software and providers. == IaaS (Infrastructure as a service) == === Providers === ==== General ==== == SaaS (Software as a Service) == === General === === Supported hosts === === Supported guests === == PaaS (Platform as a service) == === Providers === === Providers on IaaS === PaaS providers which can run on IaaS providers ("itself" means the provider is both PaaS and IaaS):
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Law practice management software

Law practice management software is software designed to manage the business operations of a law firm. This can include software that manages cases, client intake, court communications, electronic discovery, time tracking, trust accounting, and billing. == Features of law practice management software == Common features of practice management software include: Case management Time tracking Document assembly Contact management Calendaring Docket management Client portal Contract Management Court Case Status Tracker Trust accounting == Examples of law practice management software == Smokeball LEAP Legal Software PracticeEvolve Dye & Durham
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PowerBuilder

PowerBuilder is an integrated development environment owned by SAP since the acquisition of Sybase in 2010. On July 5, 2016, SAP and Appeon entered into an agreement whereby Appeon, an independent company, would be responsible for developing, selling, and supporting PowerBuilder. Over the years, PowerBuilder has been updated with new standards. In 2010, a major upgrade of PowerBuilder was released to provide support for the Microsoft .NET Framework. In 2014, support was added for OData, dockable windows, and 64-bit native applications. In 2019 support was added for rapidly creating RESTful Web APIs and non-visual .NET assemblies using the C# language and the .NET Core framework. And PowerScript client app development was revamped with new UI technologies and cloud architecture. In 2025 the IDE was revamped with new code editor and ultra-fast compiler. Appeon has been releasing new features every 6-12 month cycles, which per the product roadmap focus on four key focus areas: sustaining core features, modernizing application UI, improving developer productivity, and incorporating more Cloud technology. == Features == PowerBuilder has a native data-handling component called a DataWindow, which can be used to create, edit, and display data from a database. This object gives the programmer a number of tools for specifying and controlling user interface appearance and behavior, and also provides simplified access to database content and JSON or XML from Web services. To some extent, the DataWindow frees the programmer from considering the differences between Database Management Systems from different vendors. DataWindow can display data using multiple presentation styles and can connect to various data sources. == Usage == PowerBuilder is used primarily for building business-oriented CRUD applications. Although new software products are rarely built with PowerBuilder, many client-server ERP products and line-of-business applications built in the late 1980s to early 2000s with PowerBuilder still provide core database functions for large enterprises in government, higher education, manufacturing, insurance, banking, energy, and telecommunications. == History == === Early history === PowerBuilder originated from Computer Solutions Inc. (CSI), a software consulting firm founded in 1974 by Mitchell Kertzman in Massachusetts. CSI developed GrowthPower, an MRP II software package with integrated financial modules released in 1981, which ran exclusively on the HP 3000 platform and achieved over 1,000 customer installations at its peak. In the late 1980s, as demand increased for graphical user interfaces amid the rise of Microsoft Windows, Kertzman partnered with Dave Litwack, former executive vice president of product development at Cullinet Software (acquired by Computer Associates in 1989). Litwack joined the company in 1988 as head of research and development to develop a client/server GUI tool, leading to its rebranding as Powersoft Corporation in 1990. PowerBuilder 1.0 was released in July 1991 as a rapid application development tool featuring the DataWindow and PowerScript language. Powersoft went public on February 3, 1993, with shares closing at $38 from an initial $20 price. Sybase announced its acquisition of Powersoft on November 15, 1994, in a stock swap valued at approximately $940 million; the merger closed on February 14, 1995, at a revised value of about $904 million due to Sybase's stock fluctuations. === Recent history === In December 2013 SAP announced the new version going directly to number 15 and released a beta version. Key features included support for the .NET Framework v4.5, SQL Server 2012, Oracle 12, Windows 8, OData and Dockable Windows. SAP later released this as version 12.6. On May 31, 2019, PowerBuilder 2019 was released by Appeon. This release supports C# development. It provides a new C# IDE, .NET data access objects, C# migration solution, Web API client, and UI themes. On April 3, 2020, PowerBuilder 2019 R2 was launched by Appeon. This release includes a first-ever PowerScript-to-C# code converter, which can automatically migrate 80-95% of PowerBuilder business logic and DataWindows to C#. Interoperability between PowerScript and .NET programming languages is also now supported. Many existing features have also been enhanced. On January 22, 2021, PowerBuilder 2019 R3 was launched by Appeon. This release provides a groundbreaking new app deployment technology called PowerClient, which securely automates the installation and update of client apps over HTTPS. C# Web API development has been greatly enhanced with asynchronous programming and support for Amazon Aurora and Azure cloud databases. Aside from many other new features, PowerBuilder 2019 R3 is a long-term support (LTS) version that replaces previous LTS versions On August 6, 2021, PowerBuilder 2021 was launched by Appeon. The Cloud deployment capability of the PowerBuilder 2021 IDE, in conjunction with the matching PowerServer 2021 runtime, was revamped, bringing PowerBuilder up-to-date with the latest .NET technologies. The presentation layer now executes PowerScript natively on Windows devices. The middle-tier has been rebuilt around REST API standard with a pure .NET Core implementation. A new CI/CD utility that integrates with Git/SVN and Jenkins, witch compiles all PowerBuilder projects using the command-line interface, was added alongside other features. On September 4, 2022, PowerBuilder 2022 was launched by Appeon. This release brings enhancements to the productivity of developing both client/server & installable cloud apps and more security measures to safeguard your apps. It includes many new features, including Windows 11 support, introducing time-saving functionalities to the IDE, such as Tabbed Code Editor, Jump to Objects, and Quick Code Search, and supports the latest HTTP/2 and TLS 1.3 protocols and two-way TLS authentication. On August 4, 2023, PowerBuilder 2022 R2 was launched by Appeon. This release introduces a range of new features aimed at helping developers build powerful, feature-rich, and secure client/server and installable cloud apps more efficiently, including tabbed windows, fillable PDFs, and SMTP client. On January 8, 2024, PowerBuilder 2022 R3 was launched by Appeon. This release is a long-term support version. Features previously released in earlier releases have been enhanced and/or corrected. On May 7, 2025, PowerBuilder 2025 was launched by Appeon. This release delivers a revamped IDE that boosts developer productivity throughout the SLDC—from writing and extending code to debugging, automating builds, and deploying applications. It features a new-generation code editor, ultra-fast compiler, automatic REST API creation, faster GIT operations, and codeless UI modernization features. == Features == PowerBuilder is an object-oriented programming language. Nearly all of the visual and non-visual objects support inheritance, polymorphism, and encapsulation. The programmer may utilize a common code framework such as PowerBuilder Foundation Classes, also known as PFC, to inherit objects from and leverage pre-existing code. The DataWindow is the key component (and selling point) of PowerBuilder. The DataWindow offers a visual SQL painter which supports outer joins, unions and subquery operations. It can convert SQL to visual representation and back, so the developer can use native SQL if desired. DataWindow updates are automatic — it produces the proper SQL at runtime based on the DBMS to which the user is currently connected. This feature makes it easier for developers who are not experienced with SQL. The DataWindow also has the built-in ability to both retrieve data and update data via stored procedures or REST Web APIs as well as import/export JSON data. The RESTClient object introduced in PowerBuilder 2017 facilitates bridging the DataWindow with REST Web APIs and requiring minimal coding. === RDBMS interfaces === PowerBuilder offers native interfaces to all major databases, as well as ODBC and OLE-DB, in the Enterprise version. There are many connectivity options that allow performance monitoring and tuning, such as: Integrated security Tracing of all SQL Isolation level Password expiration dialog Blocking factor Number of SQL statements to cache Use connection pool Thread safety Trace ODBC API calls Due to the information about the database schema (such as primary key information) that are stored in PowerBuilder's data dictionary, the code required to implement data display and browsing is greatly simplified, because the dictionary information allows generation of the appropriate SQL behind the scenes. PowerBuilder supports the following ways of interacting with a database: DataWindow this is the simplest approach, relying on automatically generated SQL. Embedded SQL Embedded SQL supports SELECT, INSERT, UPDATE, DELETE and cursors. This option is used when the developer desires more control than is available with the
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WaveMaker

WaveMaker is a Java-based low-code development platform designed for building software applications and platforms. The company, WaveMaker Inc., is based in Mountain View, California. The platform is intended to assist enterprises in speeding up their application development and IT modernization initiatives through low-code capabilities. Additionally, for independent software vendors (ISVs), WaveMaker serves as a customizable low-code component that integrates into their products. The WaveMaker Platform is a licensed software platform allowing organizations to establish their own end-to-application platform-as-a-service (PaaS) for the creation and operation of custom apps. It allows developers and business users to create apps that are customizable. These applications can seamlessly consume APIs, visualize data, and automatically adapt to multi-device responsive interfaces. WaveMaker's low-code platform allows organizations to deploy applications on either public or private cloud infrastructure. Containers can be deployed on top of virtual machines or directly on bare metal. The software features a graphical user interface (GUI) console for managing IT app infrastructure, leveraging the capabilities of Docker containerization. The solution offers functionalities for automating application deployment, managing the application lifecycle, overseeing release management, and controlling deployment workflows and access permissions: Apps for web, tablet, and smartphone interfaces Enterprise technologies like Java, Hibernate, Spring, AngularJS, JQuery Docker-provided APIs and CLI Software stack packaging, container provisioning, stack and app upgrading, replication, and fault tolerance == WaveMaker Studio == WaveMaker RAD Platform is built around WaveMaker Studio, a WYSIWYG rapid development tool that allows business users to compose an application using a drag-and-drop method. WaveMaker Studio supports rapid application development (RAD) for the web, similar to what products like PowerBuilder and Lotus Notes provided for client-server computing. WaveMaker Studio allows developers to produce an application once, then automatically adjust it for a particular target platform, whether a PC, mobile phone, or tablet. Applications created using the WaveMaker Studio follow a model–view–controller architecture. WaveMaker Studio has been downloaded more than two million times. The Studio community consists of 30,000 registered users. Applications generated by WaveMaker Studio are licensed under the Apache license. Studio 8 was released on September 25, 2015. The prior version, Studio 7, has some notable development milestones. It was based on AngularJS framework, previous Studio versions (6.7, 6.6, 6.5) use the Dojo Toolkit. Some of the features WaveMaker Studio 7 include: Automatic generation of Hibernate mapping, and Hibernate queries from database schema import. Automatic creation of Enterprise Data Widgets based on schema import. Each widget can display data from a database table as a grid or edit form. Edit form implements create, update, and delete functions automatically. WYSIWYG Ajax development studio runs in a browser. Deployment to Tomcat, IBM WebSphere, Weblogic, JBoss. Mashup tool to assemble web applications based on SOAP, REST and RSS web services, Java Services and databases. Supports existing CSS, HTML and Java code. The ability to deploy a standard Java .war file. == Technologies and frameworks == WaveMaker allows users to build applications that run on "Open Systems Stack" based on the following technologies and frameworks: AngularJS, Bootstrap, NVD3, HTML, CSS, Apache Cordova, Hibernate, Spring, Spring Security, Java. The various supported integrations include: Databases: Oracle, MySQL, Microsoft SQL Server, PostgreSQL, IBM DB2, HSQLDB Authentication: LDAP, Active Directory, CAS, Custom Java Service, Database Version Control: Bitbucket (or Stash), GitHub, Apache Subversion Deployment: Amazon AWS, Microsoft Azure, WaveMaker Private Cloud (Docker containerization), IBM Web Sphere, Apache Tomcat, SpringSource tcServer, Oracle WebLogic Server, JBoss(WildFly), GlassFish App Stores: Google Play, Apple App Store, Windows Store == History == In 2003, WaveMaker was founded as ActiveGrid. Then, in 2007, it was rebranded as Wavemaker. It was acquired by VMware in 2011. In March 2013, support for the WaveMaker project was discontinued. In May 2013, Pramati Technologies acquired the assets of WaveMaker. In February 2014, Wavemaker Studio 6.7 was released, which was the last open source version of Studio. In September 2014 WaveMaker Inc. launched the WaveMaker RAD Platform, which allowed organizations to run their own application platform for building and running apps. In March 2023, WaveMaker released version 11.5, which includes enhanced low-code development capabilities and new AI-driven tools to streamline the application development process.
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Tom's Planner

Tom's Planner is a web-based tool and application service provider for project planning, management and collaboration. == History == Tom's Planner is based on Curaçao. In November 2009, it announced its public beta launch on TechCrunch and moved out of beta in August 2010. In 2013 Tom's Planner acquired its competitor Gantto. == Software == Tom's Planner is project management software that enables the creation of project schedules (Gantt charts) using a visual perspective. Tom's Planner uses the Freemium Business Model. Users can register for a free account or choose a paid version. Tom's Planner is available in five languages and is used by thousands of users on a daily basis in more than 100 countries worldwide. Customers range from fortune 500 companies to small mom-and-pop shops. == Reviews == Tom's Planner has been reviewed by PC World, TechCrunch, Lifehacker, and several other periodicals.
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Software durability

In software engineering, software durability means the solution ability of serviceability of software and to meet user's needs for a relatively long time. Software durability is important for user's satisfaction. For a software security to be durable, it must allow an organization to adjust the software to business needs that are constantly evolving, often in impulsive ways. Durability of software depends on four characteristics mainly; i.e. software trustworthiness, Human Trust for Serviceability, software dependability and software usability.
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Clarizen

Clarizen, Inc. is a project management software and collaborative work management company. Clarizen uses a software as a service business model. Clarizen's features include attaching CAD drawings to a project, moving between the project view and design view and an E-mail reporting feature. In May 2014 Clarizen raised $35 million in venture capital investment led by Goldman Sachs. The round brought investment to $90 million. Previous investors, including Benchmark Capital, Carmel Ventures, DAG Ventures, Opus Capital and Vintage Investment Partners participated. In April 2020, Clarizen appointed Matt Zilli as its new CEO, replacing Boaz Chalamish who is appointed as Executive Chairman. In January 2021 Clarizen was acquired by Planview.
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Server.com

Server.com is a domain name that was owned by software as a service (SaaS) company Server Corporation. They offered a suite of services from 1996 until 2007. It was the first SaaS site to offer a variety of services and the first to use the term WebApp to describe its services. It was selected as an Incredibly Useful Site by Yahoo! Internet Life magazine. net magazine listed Server.com among the 100 most influential websites of all time. Server.com launched in 1996 offering the first online personal information manager. In 1997, they rolled out the first threaded message board service; the first web based mailing list manager; one of the first online calendar services; and one of the first online form builders. In 2000, Server.com partnered with NBCi and became server.snap.com until 2001. In 2001, Server.com was serving 100 million monthly pageviews. Media Life declared it one of the 20 biggest ad domains on the Web. In 2002, Server.com developed one of the first web-based RSS aggregators. In 2007, all services were moved to YourWebApps.com. The domain name Server.com was sold in 2009 for $770,000.
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