Managed private cloud (also known as "hosted private cloud" or "single-tenant SaaS") refers to a principle in software architecture where a single instance of the software runs on a server, serves a single client organization (tenant), and is managed by a third party. The third-party provider is responsible for providing the hardware for the server and also for preliminary maintenance. This is in contrast to multitenancy, where multiple client organizations share a single server, or an on-premises deployment, where the client organization hosts its software instance. Managed private clouds also fall under the larger umbrella of cloud computing. == Adoption == The need for private clouds arose due to enterprises requiring a dedicated service and infrastructure for their cloud computing needs, such as for business-critical operations, improved security, and better control over their resources. Managed private cloud adoption is a popular choice among organizations. It has been on the rise due to enterprises requiring a dedicated cloud environment and preferring to avoid having to deal with management, maintenance, or future upgrade costs for the associated infrastructure and services. Such operational costs are unavoidable in on-premises private cloud data centers. == Advantages and challenges of managed private cloud == A managed private cloud cuts down on upkeep costs by outsourcing infrastructure management and maintenance to the managed cloud provider. It is easier to integrate an organization's existing software, services, and applications into a dedicated cloud hosting infrastructure which can be customized to the client's needs instead of a public cloud platform, whose hardware or infrastructure/software platform cannot be individualized to each client. Customers who choose a managed private cloud deployment usually choose them because of their desire for efficient cloud deployment, but also have the need for service customization or integration only available in a single-tenant environment. This chart shows the key benefits of the different types of deployments, and shows the overlap between these cloud solutions. This chart shows key drawbacks. Since deployments are done in a single-tenant environment, it is usually cost-prohibitive for small and medium-sized businesses. While server upkeep and maintenance are handled by the service provider, including network management and security, the client is charged for all such services. It is up to the potential client to determine if a managed private cloud solution aligns with their business objectives and budget. While the service provider maintains the upkeep of servers, network, and platform infrastructure, sensitive data is typically not stored on managed private clouds as it may leave business-critical information prone to breaches via third-party attacks on the cloud service provider. Common customizations and integrations include: Active Directory Single Sign-on Learning Management Systems Video Teleconferencing == Deployment strategies and service providers == Software companies have taken a variety of strategies in the Managed Private Cloud realm. Some software organizations have provided managed private cloud options internally, such as Microsoft. Companies that offer an on-premises deployment option, by definition, enable third-party companies to market Managed Private Cloud solutions. A few managed private cloud service providers are: Adobe Connect: Adobe Connect may be purchased for on-premises deployment, multi-tenant hosted deployment, managed private cloud as ACMS, or managed by third-party managed private cloud provider ConnectSolutions. Rackspace CenturyLink Microsoft licenses for Lync, SharePoint and Exchange may be purchased for on-premises deployment, a multi-tenant hosted deployment via Office 365, or managed by third-party cloud hosting from Azaleos, ConnectSolutions and others.
Information space analysis
Within the field of information science, information space analysis is a deterministic method, enhanced by machine intelligence, for locating and assessing resources for team-centric efforts. Organizations need to be able to quickly assemble teams backed by the support services, information, and material to do the job. To do so, these teams need to find and assess sources of services that are potential participants in the team effort. To support this initial team and resource development, information needs to be developed via analysis tools that help make sense of sets of data sources in an Intranet or Internet. Part of the process is to characterize them, partition them, and sort and filter them. These tools focus on three key issues in forming a collaborative team: Help individuals responsible for forming the team understand what is available. Assist team members in identifying the structure and categorize the information available to them in a manner specifically suited to the task at hand. Aid team members to understand the mappings of their information between their organization and that used by others who might participate. Information space analysis tools combine multiple methods to assist in this task. This causes the tools to be particularly well-suited to integrating additional technologies in order to create specialized systems.
List of C++ software and tools
This is a list of notable software and programming tools for the C++ programming language, including libraries, web frameworks, programming language implementations, compilers, integrated development environments (IDEs), and other related software development utilities. == Compilers and IDEs == AMD Optimizing C/C++ Compiler — proprietary fork of LLVM + Clang for Linux C++Builder — rapid application development (RAD) environment Clang – compiler front end for C, C++, and Objective-C, part of LLVM CLion — C++ IDE by JetBrains Code::Blocks — open-source cross-platform IDE that supports multiple compilers including GCC, Clang and Visual C++ CodeLite — cross-platform IDE for the C/C++ programming languages using the wxWidgets toolkit CodeSynthesis XSD – XML Data Binding compiler Dev-C++ — MinGW or TDM-GCC 64bit port of the GCC as its compiler GCC – GNU Compiler Collection Intel C++ Compiler – proprietary high-performance compiler by Intel KDevelop — IDE part of the KDE project and is based on KDE Frameworks and Qt, the C/C++ backend uses Clang. Microsoft Visual C++ – proprietary C++ compiler and IDE for Windows Oracle Developer Studio — Solaris, OpenSolaris, RHEL, and Oracle Linux operating systems. Qt Creator — part of the SDK for the Qt GUI application development framework and uses the Qt API SlickEdit — text editor and IDE Turbo C++ – legacy C++ IDE and compiler popular in the 1990s Understand — IDE that enables static code analysis through an array of visuals, documentation, and metric tools. Visual Studio — integrated development environment by Microsoft that supports C++ Visual Studio Code — integrated development environment by Microsoft that supports C++ Xcode — Apple IDE to develop macOS, iOS, iPadOS, watchOS, tvOS, and visionOS that supports C++ source code. == Debuggers == Allinea DDT – a graphical debugger dbx — a proprietary source-level debugger GNU Debugger – portable debugger that runs on many Unix-like systems Modular Debugger — a C/C++ source level debugger for Solaris and derivates Undo LiveRecorder — time travel debugger == Libraries == Active Template Library – template-based C++ classes developed by Microsoft Apache MXNet — deep learning framework Apache Xerces – parsing, validating, and serializing and manipulating XML. Asio — networking and low-level I/O library Bitpit — scientific computing and mesh manipulation library Boost — collection of peer-reviewed libraries Botan — cryptography library C++ AMP – easy way to write programs that compile and execute on data-parallel hardware, such as graphics cards and GPUs C++ Standard Library — standard library for the language C++/WinRT — library for Microsoft's Windows Runtime platform, designed to provide access to modern Windows APIs. C3D Toolkit — geometric modeling kernel Caffe — deep learning framework CAPD — library for rigorous numerics and dynamical systems Cassowary — constraint-solving toolkit that efficiently solves systems of linear equalities and inequalities Cinder — library for creative coding ClanLib — cross-platform game SDK CMU Sphinx — speech recognition system Crypto++ — cryptographic algorithms library Dlib — general-purpose cross-platform library Dune — partial differential equations using grid-based methods fastText — text representation and text classification library FLTK — GUI toolkit Geospatial Data Abstraction Library — geospatial data access library GDCM — image library General Polygon Clipper — polygon clipping library GiNaC — computer algebra system that uses Class Library for Numbers for implementing arbitrary-precision arithmetic GLFW — OpenGL and window management library HarfBuzz — text rendering and typesetting library High Efficiency Image File Format — digital container format for storing individual digital images and image sequences ITK — image analysis library Integrated Performance Primitives — domain-specific functions that are highly optimized for diverse Intel architectures Jackets library — GPU computing library JSBSim — open-source flight dynamics model JUCE — framework for audio applications KDE Frameworks — collection of libraries from the KDE project KFRlib — digital signal processing framework LEMON — library for optimization and graph problems LevelDB — key–value database library Libdash — MPEG-DASH streaming library libLAS — reading and writing geospatial data encoded in the ASPRS laser (LAS) file format libsigc++ — typesafe callbacks LibRaw — free and open-source software library for reading raw files from digital cameras libSBML — application programming interface (API) for the SBML (Systems Biology Markup Language) LIBSVM — sequential minimal optimization (SMO) algorithm for kernelized support vector machines Libx — DirectX .X files graphics library Loki — collection of design patterns LIVE555 — multimedia streaming library Metakit — embedded database library Microsoft Cognitive Toolkit — deep learning toolkit Microsoft Foundation Class Library — object-oriented library for developing desktop applications for Windows Microsoft SEAL — homomorphic encryption library mlpack — machine learning and AI library Mobile Robot Programming Toolkit — robotics research library Object Windows Library — Object Windows Library, superseded by VCL Open Cascade — CAD and 3D modeling library Open Asset Import Library — 3D model import library to provide a common API for different 3D asset file formats OpenCV – computer vision and machine learning library OpenFOAM — computational fluid dynamics toolkit OpenH264 — real-time encoding and decoding video streams in the H.264/MPEG-4 AVC format OpenImageIO — image processing library Open Inventor — higher layer of programming for OpenGL OpenNN — neural networks library OpenVDB — sparse volume data library openFrameworks — creative coding toolkit OpenRTM-aist — robotics middleware library Oracle Template Library — database access that supports IBM Db2 and Open Database Connectivity Orfeo toolbox — remote sensing image processing library OR-Tools — operations research and optimization library Parallel Augmented Maps — ordered sets, ordered maps, and augmented maps. Parallel Patterns Library — Microsoft library that provides features for multicore programming PhysX — physics simulation engine POCO C++ Libraries — general-purpose libraries for software development Poppler — PDF rendering library Protocol Buffers — data serialization library Qt — cross-platform widget toolkit QuantLib — quantitative finance library RocksDB — key–value database library ROOT — data analysis framework from CERN ROS — robotics middleware Scintilla — source code editing component SDL – Simple DirectMedia Layer, cross-platform development library for multimedia applications SFML – Simple and Fast Multimedia Library Shark – open-source machine learning library Shogun — machine learning toolbox Skia — 2D graphics library Snappy — compression library Sound Object Library — music and audio development Standard Template Library — library of containers and algorithms Stapl — parallel computing library SymbolicC++ — symbolic computation library TerraLib — GIS library Tesseract OCR — optical character recognition engine Threading Building Blocks — parallel computing library ThreadWeaver — concurrency framework Tiny-dnn — lightweight deep learning library TinyXML — lightweight XML parser Tkrzw — key–value databases VTD-XML — XML processing library wxWidgets — cross-platform GUI toolkit x265 — video encoding library for HEVC XGBoost — gradient boosting library Windows Template Library — Win32 development === Mathematical and numerical libraries === == Tools == Akonadi — a C++/Qt framework and storage service for personal information management BALL – framework and set of algorithms and data structures for molecular modelling and computational structural bioinformatics Boehm garbage collector – conservative garbage collector CEGUI — C++ GUI library ClanLib – video game SDK CMake — cross-platform build system for C++ projects Confidential Consortium Framework – blockchain infrastructure framework DaviX – WebDAV client Doxygen — documentation generator for C++ and other languages FLTK — Fast Light Toolkit, cross-platform GUI library Fox toolkit — C++ GUI toolkit GDB — GNU Project debugger, often used with C and C++ gtkmm — official C++ interface for the popular GUI library GTK HOOPS Visualize — 3D computer graphics HPX — partitioned global address space Parallel programming Runtime System JUCE — cross-platform C++ audio and GUI framework LessTif — free clone of Motif GUI toolkit MFC — Microsoft Foundation Class library Nana — modern C++ GUI toolkit PTK Toolkit — 2D rendering engine and SDK, and portability options. Qt — cross-platform C++ GUI toolkit Rogue Wave — C++ GUI toolkit TnFOX — C++ GUI toolkit Ultimate++ — cross-platform C++ GUI framework Valgrind — tool suite for debugging and profiling C/C++ programs wxWidgets — cross-platform C++ GUI toolkit x265 — encoder for creating digital video streams in the High Efficiency Vid
System context diagram
A system context diagram in engineering is a diagram that defines the boundary between the system, or part of a system, and its environment, showing the entities that interact with it. This diagram is a high level view of a system. It is similar to a block diagram. == Overview == System context diagrams show a system, as a whole and its inputs and outputs from/to external factors. According to Kossiakoff and Sweet (2011): System Context Diagrams ... represent all external entities that may interact with a system ... Such a diagram pictures the system at the center, with no details of its interior structure, surrounded by all its interacting systems, environments and activities. The objective of the system context diagram is to focus attention on external factors and events that should be considered in developing a complete set of systems requirements and constraints. System context diagrams are used early in a project to get agreement on the scope under investigation. Context diagrams are typically included in a requirements document. These diagrams must be read by all project stakeholders and thus should be written in plain language, so the stakeholders can understand items within the document. == Building blocks == Context diagrams can be developed with the use of two types of building blocks: Entities (Actors): labeled boxes; one in the center representing the system, and around it multiple boxes for each external actor Relationships: labeled lines between the entities and system For example, "customer places order." Context diagrams can also use many different drawing types to represent external entities. They can use ovals, stick figures, pictures, clip art or any other representation to convey meaning. Decision trees and data storage are represented in system flow diagrams. A context diagram can also list the classifications of the external entities as one of a set of simple categories (Examples:), which add clarity to the level of involvement of the entity with regards to the system. These categories include: Active: Dynamic to achieve some goal or purpose (Examples: "Article readers" or "customers"). Passive: Static external entities which infrequently interact with the system (Examples: "Article editors" or "database administrator"). Cooperative: Predictable external entities which are used by the system to bring about some desired outcome (Examples: "Internet service providers" or "shipping companies"). Autonomous (Independent): External entities which are separated from the system, but affect the system indirectly, by means of imposed constraints or similar influences (Examples: "regulatory committees" or "standards groups"). == Alternatives == The best system context diagrams are used to display how a system interoperates at a very high level, or how systems operate and interact logically. The system context diagram is a necessary tool in developing a baseline interaction between systems and actors; actors and a system or systems and systems. Alternatives to the system context diagram are: Architecture Interconnect Diagram: The figure gives an example of an Architecture Interconnect Diagram: A representation of the Albuquerque regional ITS architecture interconnects for the Albuquerque Police Department that was generated using the Turbo Architecture tool is shown in the figure. Each block represents an ITS inventory element, including the name of the stakeholder in the top shaded portion. The interconnect lines between elements are solid or dashed, indicating existing or planned connections. Business Model Canvas, a strategic management template for developing new or documenting existing business models. It is a visual chart with elements describing a firm's value proposition, infrastructure, customers, and finances.[1] It assists firms in aligning their activities by illustrating potential trade-offs. Enterprise data model: this type of data model according to Simsion (2005) can contain up to 50 to 200 entity classes, which results from specific "high level of generalization in data modeling". IDEF0 Top Level Context Diagram: The IDEF0 process starts with the identification of the prime function to be decomposed. This function is identified on a "Top Level Context Diagram" that defines the scope of the particular IDEF0 analysis. Problem Diagrams (Problem Frames): In addition to the kinds of things shown on a context diagram, a problem diagram shows requirements and requirements references. Use case diagram: One of the Unified Modeling Language diagrams. They also represent the scope of the project at a similar level of abstraction. - Use Cases, however, tend to focus more on the goals of 'actors' who interact with the system, and do not specify any solution. Use Case diagrams represent a set of Use Cases, which are textual descriptions of how an actor achieves the goal of a use case. for Example Customer Places Order. ArchiMate: ArchiMate is an open and independent enterprise architecture modeling language to support the description, analysis and visualization of architecture within and across business domains in an unambiguous way. Most of these diagrams work well as long as a limited number of interconnects will be shown. Where twenty or more interconnects must be displayed, the diagrams become quite complex and can be difficult to read.
Caspio
Caspio, Inc. is an American software company providing a low-code platform for building cloud-based business applications. Founded in 2000 by Frank Zamani, the company is headquartered in Sunnyvale, California, with operations in Poland, the Philippines, and Spain. Caspio’s platform allows organizations to create online database applications and workflow tools without extensive coding. == History == Caspio was founded by Frank Zamani in 2000. The company initially focused on simplifying custom cloud applications and reducing development time and cost as compared to traditional software development. Caspio released the first version of its platform, Caspio Bridge, in 2001. In 2014, Caspio released a HIPAA-Compliant Edition of its low-code application development platform. Caspio also released an EU General Data Protection Regulation (GDPR) Compliance Edition of its low-code application development platform in 2016. Caspio's second European Software Development Center opened in Kraków, Poland in 2017. In 2019, Forrester Research listed Caspio and three other platforms in its highest of four ranked tiers of twelve low-code platforms for business developers based on rankings of offerings and strategy at that time. Caspio also opened data centers in Montreal, Canada and India in 2020.
Question answering
Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language. A question-answering implementation, usually a computer program, may construct its answers by querying a structured database of knowledge or information, usually a knowledge base. More commonly, question-answering systems can pull answers from an unstructured collection of natural language documents. Some examples of natural language document collections used for question answering systems include reference texts, compiled newswire reports, Wikipedia pages and other World Wide Web pages. == History == Two early question answering systems were BASEBALL and LUNAR. BASEBALL answered questions about Major League Baseball over a period of one year. LUNAR answered questions about the geological analysis of rocks returned by the Apollo Moon missions. Both question answering systems were very effective in their chosen domains. LUNAR was demonstrated at a lunar science convention in 1971 and it was able to answer 90% of the questions in its domain that were posed by people untrained on the system. Further restricted-domain question answering systems were developed in the following years. The common feature of all these systems is that they had a core database or knowledge system that was hand-written by experts of the chosen domain. The language abilities of BASEBALL and LUNAR used techniques similar to ELIZA and DOCTOR, the first chatterbot programs. SHRDLU was a successful question-answering program developed by Terry Winograd in the late 1960s and early 1970s. It simulated the operation of a robot in a toy world (the "blocks world"), and it offered the possibility of asking the robot questions about the state of the world. The strength of this system was the choice of a very specific domain and a very simple world with rules of physics that were easy to encode in a computer program. In the 1970s, knowledge bases were developed that targeted narrower domains of knowledge. The question answering systems developed to interface with these expert systems produced more repeatable and valid responses to questions within an area of knowledge. These expert systems closely resembled modern question answering systems except in their internal architecture. Expert systems rely heavily on expert-constructed and organized knowledge bases, whereas many modern question answering systems rely on statistical processing of a large, unstructured, natural language text corpus. The 1970s and 1980s saw the development of comprehensive theories in computational linguistics, which led to the development of ambitious projects in text comprehension and question answering. One example was the Unix Consultant (UC), developed by Robert Wilensky at U.C. Berkeley in the late 1980s. The system answered questions pertaining to the Unix operating system. It had a comprehensive, hand-crafted knowledge base of its domain, and it aimed at phrasing the answer to accommodate various types of users. Another project was LILOG, a text-understanding system that operated on the domain of tourism information in a German city. The systems developed in the UC and LILOG projects never went past the stage of simple demonstrations, but they helped the development of theories on computational linguistics and reasoning. Specialized natural-language question answering systems have been developed, such as EAGLi for health and life scientists. Question answering systems have been extended in recent years to encompass additional domains of knowledge For example, systems have been developed to automatically answer temporal and geospatial questions, questions of definition and terminology, biographical questions, multilingual questions, and questions about the content of audio, images, and video. Current question answering research topics include: interactivity—clarification of questions or answers answer reuse or caching semantic parsing answer presentation knowledge representation and semantic entailment social media analysis with question answering systems sentiment analysis utilization of thematic roles Image captioning for visual question answering Embodied question answering In 2011, Watson, a question answering computer system developed by IBM, competed in two exhibition matches of Jeopardy! against Brad Rutter and Ken Jennings, winning by a significant margin. Facebook Research made their DrQA system available under an open source license. This system uses Wikipedia as knowledge source. The open source framework Haystack by deepset combines open-domain question answering with generative question answering and supports the domain adaptation of the underlying language models for industry use cases. Large Language Models (LLMs)[36] like GPT-4[37], Gemini[38] are examples of successful QA systems that are enabling more sophisticated understanding and generation of text. When coupled with Multimodal[39] QA Systems, which can process and understand information from various modalities like text, images, and audio, LLMs significantly improve the capabilities of QA systems. == Types == Question-answering research attempts to develop ways of answering a wide range of question types, including fact, list, definition, how, why, hypothetical, semantically constrained, and cross-lingual questions. Answering questions related to an article in order to evaluate reading comprehension is one of the simpler form of question answering, since a given article is relatively short compared to the domains of other types of question-answering problems. An example of such a question is "What did Albert Einstein win the Nobel Prize for?" after an article about this subject is given to the system. Closed-book question answering is when a system has memorized some facts during training and can answer questions without explicitly being given a context. This is similar to humans taking closed-book exams. Closed-domain question answering deals with questions under a specific domain (for example, medicine or automotive maintenance) and can exploit domain-specific knowledge frequently formalized in ontologies. Alternatively, "closed-domain" might refer to a situation where only a limited type of questions are accepted, such as questions asking for descriptive rather than procedural information. Question answering systems in the context of machine reading applications have also been constructed in the medical domain, for instance related to Alzheimer's disease. Open-domain question answering deals with questions about nearly anything and can only rely on general ontologies and world knowledge. Systems designed for open-domain question answering usually have much more data available from which to extract the answer. An example of an open-domain question is "What did Albert Einstein win the Nobel Prize for?" while no article about this subject is given to the system. Another way to categorize question-answering systems is by the technical approach used. There are a number of different types of QA systems, including: rule-based systems, statistical systems, and hybrid systems. Rule-based systems use a set of rules to determine the correct answer to a question. Statistical systems use statistical methods to find the most likely answer to a question. Hybrid systems use a combination of rule-based and statistical methods. == Architecture == As of 2001, question-answering systems typically included a question classifier module that determined the type of question and the type of answer. Different types of question-answering systems employ different architectures. For example, modern open-domain question answering systems may use a retriever-reader architecture. The retriever is aimed at retrieving relevant documents related to a given question, while the reader is used to infer the answer from the retrieved documents. Systems such as GPT-3, T5, and BART use an end-to-end architecture in which a transformer-based architecture stores large-scale textual data in the underlying parameters. Such models can answer questions without accessing any external knowledge sources. == Methods == Question answering is dependent on a good search corpus; without documents containing the answer, there is little any question answering system can do. Larger collections generally mean better question answering performance, unless the question domain is orthogonal to the collection. Data redundancy in massive collections, such as the web, means that nuggets of information are likely to be phrased in many different ways in differing contexts and documents, leading to two benefits: If the right information appears in many forms, the question answering system needs to perform fewer complex NLP techniques to understand the text. Correct answers can be filtered from false positives because the syst
Zoho Office Suite
Zoho Office Suite is an online office suite developed by Zoho Corporation. == History == Zoho Office Suite was launched in 2005 with a web-based word processor. Additional products, such as those for spreadsheets and presentations, were incorporated later into the suite. The applications are distributed as software as a service (SaaS). == Products == Zoho uses an open API for its Writer, Sheet, Show, Creator, Meeting, and Planner products. It also has plugins into Microsoft Word and Excel, an OpenOffice.org plugin, and a plugin for Firefox. Zoho Office Suite is free for individuals but offers a plan for teams, which includes Zoho WorkDrive, Zoho Workplace and other Zoho apps. In October 2009, Zoho integrated some of their applications with the Google Apps online suite.