A.i. And Xr Marketing

A.i. And Xr Marketing — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Tweak programming environment

Tweak is a graphical user interface (GUI) layer written by Andreas Raab for the Squeak development environment, which in turn is an integrated development environment based on the Smalltalk-80 computer programming language. Tweak is an alternative to an earlier graphic user interface layer called Morphic. Development began in 2001. Applications that use the Tweak software include Sophie (version 1), a multimedia and e-book authoring system, and a family of virtual world systems: Open Cobalt, Teleplace, OpenQwaq, 3d ICC's Immersive Terf and the Croquet Project. == Influences == An experimental version of Etoys, a programming environment for children, used Tweak instead of Morphic. Etoys was a major influence on a similar Squeak-based programming environment known as Scratch.
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Data custodian

In data governance groups, responsibilities for data management are increasingly divided between the business process owners and information technology (IT) departments. Two functional titles commonly used for these roles are data steward and data custodian. Data Stewards are commonly responsible for data content, context, and associated business rules. Data custodians are responsible for the safe custody, transport, storage of the data and implementation of business rules. Simply put, Data Stewards are responsible for what is stored in a data field, while data custodians are responsible for the technical environment and database structure. Common job titles for data custodians are database administrator (DBA), data modeler, ETL developer and data engineer. == Data custodian responsibilities == A data custodian ensures: Access to the data is authorized and controlled Data stewards are identified for each data set Technical processes sustain data integrity Processes exist for data quality issue resolution in partnership with data stewards Technical controls safeguard data Data added to data sets are consistent with the common data model Versions of master data are maintained along with the history of changes Change management practices are applied in maintenance of the database Data content and changes can be audited
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Customer data management

Customer data management (CDM) is the ways in which businesses keep track of their customer information and survey their customer base in order to obtain feedback. CDM includes a range of software or cloud computing applications designed to give large organizations rapid and efficient access to customer data. Surveys and data can be centrally located and widely accessible within a company, as opposed to being warehoused in separate departments. CDM encompasses the collection, analysis, organizing, reporting and sharing of customer information throughout an organization. Businesses need a thorough understanding of their customers’ needs if they are to retain and increase their customer base. Efficient CDM solutions provide companies with the ability to deal instantly with customer issues and obtain immediate feedback. As a result, customer retention and customer satisfaction can show marked improvement. According to a study by Aberdeen Group, "above-average and best-in-class companies... attain greater than 20% annual improvement in retention rates, revenues, data accuracy and partner/customer satisfaction rates." == Customer data management and cloud computing == Cloud computing offers an attractive choice for CDM in many companies due to its accessibility and cost-effectiveness. Businesses can decide who, within their company, should have the ability to create, adjust, analyze or share customer information. In December 2010, 52% of Information Technology (IT) professionals worldwide were deploying, or planning to deploy, cloud computing; this percentage is far higher in many countries. == Background == Customer data management, as a term, was coined in the 1990s, pre-dating the alternative term enterprise feedback management (EFM). CDM was introduced as a software solution that would replace earlier disc-based or paper-based surveys and spreadsheet data. Initially, CDM solutions were marketed to businesses as software, which were specific to one company, and often to one department within that company. This was superseded by application service providers (ASPs) where software was hosted for end user organizations, thus avoiding the necessity for IT professionals to deploy and support software. However, ASPs with their single-tenancy architecture were, in turn, superseded by software as a service (SaaS), engineered for multi-tenancy. By 2007 SaaS applications, giving businesses on-demand access to their customer information, were rapidly gaining popularity compared with ASPs. Cloud computing now includes SaaS and many prominent CDM providers offer cloud-based applications to their clients. In recent years, there has been a push away from the term EFM, with many of those working in this area advocating the slightly updated use of CDM. The return to the term CDM is largely based on the greater need for clarity around the solutions offered by companies, and on the desire to retire terminology veering on techno-jargon that customers may have a hard time understanding.
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Social media surgery

A social media surgery is a gathering at which volunteer "surgeons" with expertise in using web tools, chiefly social media, offer free advice in using such tools, to representatives ("patients") of non-profit organisations, charities, community groups and activists, with "no boring speeches or jargon". The idea was conceived by Pete Ashton, with Nick Booth of Podnosh Ltd, who ran the first such surgery in Birmingham, England, on 15 October 2008. In July 2009, a spin-off surgery (dubbed the "Social media mob") started in Mosman, Australia, and in January 2010, the first spin-off surgery in Africa was held. On 16 February 2012, it was announced that the Social Media Surgery movement had won "the Prime Minister’s Big Society Award". Prime Minister David Cameron said: This is an excellent initiative - such a simple idea and yet so effective. The popularity of these surgeries and the fact that they have inspired so many others across the country to follow in their footsteps, is testament to its brilliance. Congratulations to Nick and all the volunteers who have shared their time and expertise to help so many local groups make the most of the internet to support their community. A great example of the Big Society in action. The scheme also won the 2013 Adult Learners' Week "BBC Learning Through Technology Award".
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Commission on Enhancing National Cybersecurity

The President's Commission on Enhancing National Cybersecurity is a Presidential Commission formed on April 13, 2016, to develop a plan for protecting cyberspace, and America's economic reliance on it. The commission released its final report in December 2016. The report made recommendations regarding the intertwining roles of the military, government administration and the private sector in providing cyber security. Chairman Donilon said of the report that its coverage "is unusual in the breadth of issues" with which it deals. == Recommendations == The report made sixteen major recommendations with fifty-three specific action items broadly grouped under six areas: Protecting the information and digital infrastructure Investing in the secure growth of information and digital infrastructure Consumer information access Building the cybersecurity workforce Building a secure governmental cybersecurity framework Keeping interconnectivity open, fair, competitive, and secure The Commission found that strong authentication systems were mandatory for adequate cybersecurity, not just for the government, but for all commercial systems, and private individuals. The commission also stressed remote identity proofing and security for the Internet of things (IoT). Finding that technicians who know cybersecurity and can protect systems are few and in short supply, the commission recommended nationally supported training programs to produce an adequate workforce, as well as increasing the level of expertise in the existing workforce. The Commission highlighted the importance of partnerships between government and the private sector as a powerful tool for encouraging the technology, policies and practices we need to secure and grow the digital economy. (page 2) Some criticised the commission's work as lacking an understanding of cybersecurity and not being cognizant of "cyber reality" and the cost of some of the action items, but others found the report constructive and meaningful. == Commission members == The initial members of the Commission are: Tom Donilon, former Assistant to the President and National Security Advisor (Chair) Sam Palmisano, former CEO of IBM (Vice Chair) General Keith Alexander, CEO of IronNet Cybersecurity, former Director of the National Security Agency and former Commander of U.S. Cyber Command Annie Antón, Professor and Chair of the School of Interactive Computing at Georgia Tech. Ajay Banga, President and CEO of MasterCard Steven Chabinsky, General Counsel and Chief Risk Officer of CrowdStrike Patrick Gallagher, Chancellor of the University of Pittsburgh and former Director of the National Institute of Standards and Technology Peter Lee, Corporate Vice President, Microsoft Research Herbert Lin, Senior Research Scholar for Cyber Policy and Security at the Stanford Center for International Security and Cooperation and Research Fellow at the Hoover Institution Heather Murren, former member of the Financial Crisis Inquiry Commission and co-founder of the Nevada Cancer Institute Joe Sullivan, Chief Security Officer of Uber and former Chief Security Officer of Facebook Maggie Wilderotter, Executive Chairman of Frontier Communications == Follow-on == Incoming President Trump has indicated that he wants a full review of U.S. cyber protection policy. == Notes and references ==
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Philco computers

Philco was one of the pioneers of transistorized computers, also known as second-generation computers. After the company developed the surface-barrier transistor, which was much faster than previous point-contact types, it was awarded contracts for military and government computers. Commercialized derivatives of some of these designs became successful business and scientific computers. The TRANSAC (Transistor Automatic Computer) Model S-1000 was released as a scientific computer. The TRANSAC S-2000 mainframe computer system was first produced in 1958, and a family of compatible machines, with increasing performance, was released over the next several years. However, the mainframe computer market was dominated by IBM. Other companies could not deploy resources for development, customer support and marketing on the scale that IBM could afford, making competition in this segment difficult after the introduction of the IBM 360 family. Philco went bankrupt and was purchased in 1961 by Ford Motor Company, but the computer division carried on until the Philco division of Ford exited the computer business in 1963. The Ford company maintained one Philco mainframe in use until 1981. == The surface-barrier transistor == The surface-barrier transistor developed by Philco in 1953 had a much higher frequency response than the original point-contact transistors. The transistor was made of a thin crystal of germanium, which was electrolytically etched with pits on either side forming a very thin base region, on the order of 5 micrometers. Philco's process for etching was United States patent number 2,885,571. Philco surface-barrier transistors were used in TX-0, and in early models of what would become the DEC PDP product line. Although relatively fast, the small size of the devices limited their power to circuits operating at a few tens of milliwatts. == Military and government == Between 1955 and 1957, Philco built transistor computers for use in aircraft, models C-1000, C-1100, and C-1102, intended for airborne real-time applications. By 1957, the C-1102 had been used by a civilian sector customer. The BASICPAC AN/TYK 6V (first delivery in 1961), COMPAC AN/TYK 4V (not completed), and LOGICPAC systems were built for the US Army as transportable computer systems for use with their Fieldata concept of integrated information management. BASICPAC was a transistorized computer with up to 28,672 words of 38-bit core memory (including sign and parity), available in several configurations from a minimum system, to a truck-borne mobile version, to a fully expanded system. Basic clock periods was 1 microsecond (which gives a clock rate of 1 MHz), with 12 microsecond memory access and a fixed-point multiplication taking 242 microseconds. Input/output was by paper tape reader and punch, or through a teletypewriter. With additional hardware, magnetic tape storage was also available, with up to seven I/O devices. The instruction set had 31 basic operation codes and nine opcodes for I/O === CXPQ === Philco was contracted by the US Navy to build the CXPQ computer. One model was completed and installed at the David Taylor Model Basin. This design was later adapted to become the commercial TRANSAC S-2000. Only one CXPQ was built. The CXPQ is a 48-bit transistorized computer. === SOLO === In 1955, the National Security Agency through the US Navy contracted with Philco to produce a computer suitable for use as a workstation, with an architecture based on the vacuum-tube computer system called Atlas II already in use at the NSA, and similar to the commercial UNIVAC 1103. At the time, Philco was the largest producer of surface barrier transistors, which were the only type available with the speed and quantities required for a computer. The SOLO prototype was delivered in 1958, but required extensive debugging at NSA. Difficulties were encountered with core memory and power supplies. SOLO used paper tape and teleprinter machines for input and output. SOLO cost about $1 million US, and contained 8,000 transistors. While the system was extensively used for training, testing, research and development, no additional units were ordered. SOLO was removed from active service in 1963. The design of the SOLO became commercialized as Philco's TRANSAC Model S-1000. == Commercial == === S-1000 === The TRANSAC S-1000 was a scientific computer with a 36-bit word length and 4096 words of core memory. It was packaged in a container about the size of a large office desk, and used only 1.2 kilowatts, much less than vacuum-tube-based computers of similar capacity. In a 1961 survey, about 15 S-1000 computer installations had been identified. It weighed about 1,650 pounds (750 kg). === S-2000 === The TRANSAC S-2000 was a large mainframe system intended for both business and scientific work. It had a 48-bit word length and supported calculations in fixed point, floating point and binary-coded decimal formats. The original S-2000 "TRANSAC" (Transistor Automatic Computer) released in 1958 was later designated Model 210; it was used internally at Philco. Similar to the Control Data Corporation Model 1604, it was a 48-bit fully transistorized computer. Three succeeding models were released in the series, all compatible with the software of the original model. The Model 211 was introduced in 1960, using micro-alloy diffused field-effect transistors, requiring significant redesign of circuits compared to the original. The TRANSAC S-2000/Philco 210/211 weighed about 2,000 pounds (910 kg). By 1964, eighteen Model 210, eighteen Model 211 and seven Model 212 systems had been sold. After Philco was purchased by Ford Motor Company, the Model 212 was introduced in 1962 and released in 1963. It had 65,535 words of 48-bit memory. Initially made with 6-microsecond core memory, it had better performance than the IBM 7094 transistor computer. It was later upgraded in 1964 to 2-microsecond core memory, which gave the machine floating-point performance greater than the IBM 7030 Stretch computer. A Model 213 was announced in 1964 but never built. By that time competition from IBM had made the Philco computer operations no longer profitable for Ford, and the division was closed down. The Model 212 could carry out a floating-point multiplication in 22 microseconds. Each word contained two 24-bit instructions with 16 bits of address information and eight bits for the opcode. There were 225 different valid opcodes in the Model 212; invalid opcodes were detected and halted the machine. The CPU had an accumulator register of 48 bits, three general-purpose registers of 24 bits, and 32 index registers of 15 bits. Main memory size ranged from 4K words to 64K words. Only the first model had a magnetic drum memory; later editions used tape drives. The Model 212 weighed about 6,500 pounds (3.3 short tons; 2.9 t). Software for the S-2000 initially consisted of TAC (Translator-Assembler-Compiler), and ALTAC, a FORTRAN II-like language with some differences from the IBM 704 FORTRAN implementation. A COBOL compiler was also available, targeted at business applications. The Philco 2400 was the input/output system for the S-2000. Operations such as reading cards or printing were carried out through magnetic tapes, thereby offloading the S-2000 from relatively slow input/output processing. The 2400 had a 24-bit word length and could be supplied with 4K to 32K characters (1K to 8K words) of core memory, rated at 3-microsecond cycle time. The instruction set was aimed at character I/O use. The idea of base registers, implemented in Philco computers, influenced the design of IBM/360. The last Philco TRANSAC S-2000 Model 212 was taken out of service in December 1981, after 19 years of service at Ford.
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Hyper-encryption

Hyper-encryption is a form of encryption invented by Michael O. Rabin which uses a high-bandwidth source of public random bits, together with a secret key that is shared by only the sender and recipient(s) of the message. It uses the assumptions of Ueli Maurer's bounded-storage model as the basis of its secrecy. Although everyone can see the data, decryption by adversaries without the secret key is still not feasible, because of the space limitations of storing enough data to mount an attack against the system. Unlike almost all other cryptosystems except the one-time pad, hyper-encryption can be proved to be information-theoretically secure, provided the storage bound cannot be surpassed. Moreover, if the necessary public information cannot be stored at the time of transmission, the plaintext can be shown to be impossible to recover, regardless of the computational capacity available to an adversary in the future, even if they have access to the secret key at that future time. A highly energy-efficient implementation of a hyper-encryption chip was demonstrated by Krishna Palem et al. using the Probabilistic CMOS or PCMOS technology and was shown to be ~205 times more efficient in terms of Energy-Performance-Product.
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ServerNet

ServerNet is a switched fabric communications link primarily used in proprietary computers made by Tandem Computers, Compaq, and HP. Its features include good scalability, clean fault containment, error detection and failover. The ServerNet architecture specification defines a connection between nodes, either processor or high performance I/O nodes such as storage devices. == History == Tandem Computers developed the original ServerNet architecture and protocols for use in its own proprietary computer systems starting in 1992, and released the first ServerNet systems in 1995. Early attempts to license the technology and interface chips to other companies failed, due in part to a disconnect between the culture of selling complete hardware / software / middleware computer systems and that needed for selling and supporting chips and licensing technology. A follow-on development effort ported the Virtual Interface Architecture to ServerNet with PCI interface boards connecting personal computers. Infiniband directly inherited many ServerNet features. As of 2017, systems still ship based on the ServerNet architecture.
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Cloud-based quantum computing

Cloud-based quantum computing refers to the remote access of quantum computing resources—such as quantum emulators, simulators, or processors—via the internet. Cloud access enables users to develop, test, and execute quantum algorithms without the need for direct interaction with specialized hardware, facilitating broader participation in quantum software development and experimentation. In 2016, IBM launched the IBM Quantum Experience, one of the first publicly accessible quantum processors connected to the cloud. In early 2017, researchers at Rigetti Computing demonstrated programmable quantum cloud access through their software platform Forest, which included the pyQuil Python library. Since the early-2020s, cloud-based quantum computing has grown significantly, with multiple providers offering access to a variety of quantum hardware modalities, including superconducting qubits, trapped ions, neutral atoms, and photonic systems. Major platforms such as Amazon Braket, Azure Quantum, and qBraid aggregate quantum devices from hardware developers like IonQ, Rigetti Computing, QuEra, Pasqal, Oxford Quantum Circuits, and IBM Quantum. These platforms provide unified interfaces for users to write and execute quantum algorithms across diverse backends, often supporting open-source SDKs such as Qiskit, Cirq, and PennyLane. The proliferation of cloud-based access has played a key role in accelerating quantum education, algorithm research, and early-stage application development by lowering the barrier to experimentation with real quantum hardware. Cloud-based quantum computing has expanded access to quantum hardware and tools beyond traditional research laboratories. These platforms support educational initiatives, algorithm development, and early-stage commercial applications. == Applications == Cloud-based quantum computing is used across education, research, and software development, offering remote access to quantum systems without the need for on-site infrastructure. === Education === Quantum cloud platforms have become valuable tools in education, allowing students and instructors to engage with real quantum processors through user-friendly interfaces. Educators use these platforms to teach foundational concepts in quantum mechanics and quantum computing, as well as to demonstrate and implement quantum algorithms in a classroom or laboratory setting. === Scientific Research === Cloud-based access to quantum hardware has enabled researchers to conduct experiments in quantum information, test quantum algorithms, and compare quantum hardware platforms. Experiments such as testing Bell's theorem or evaluating quantum teleportation protocols have been performed on publicly available quantum processors. === Software Development and Prototyping === Developers use cloud-based platforms to prototype quantum software applications across fields such as optimization, machine learning, and chemistry. These platforms offer SDKs and APIs that integrate classical and quantum workflows, enabling experimentation with quantum algorithms in real-world or simulated environments. === Public Engagement and Games === Quantum cloud tools have also been used to create educational games and interactive applications aimed at increasing public understanding of quantum concepts. These efforts help bridge the gap between theoretical content and intuitive learning. == Existing platforms == qBraid Lab by qBraid is a cloud-based platform for quantum computing. It provides software tools for researchers and developers in quantum, as well as access to quantum hardware. qBraid provides cloud based access to Microsoft Azure Quantum and Amazon Braket devices including IQM, QuEra, Pasqal, Rigetti, IonQ, QIR simulators, Amazon Braket simulators, and the NEC Vector Annealer, as of August 2025. qBraid's base version is free, where unlimited hardware and simulator access is available with the purchase of credits. Quandela Cloud by Quandela is the platform to access first cloud-accessible European photonic quantum computer. The computer is interfaced using the Perceval scripting language, with tutorials and documentation available online for free. Xanadu Quantum Cloud by Xanadu is a platform with cloud-based access to three fully programmable photonic quantum computers. Forest by Rigetti Computing is a tool suite for cloud-based quantum computing. It includes a programming language, development tools and example algorithms. LIQUi> by Microsoft is a software architecture and tool suite for quantum computing. It includes a programming language, example optimization and scheduling algorithms, and quantum simulators. Q#, a quantum programming language by Microsoft on the .NET Framework seen as a successor to LIQUi|>. IBM Quantum Platform by IBM, providing access to quantum hardware as well as HPC simulators. These can be accessed programmatically using the Python-based Qiskit framework, or via graphical interface with the IBM Q Experience GUI. Both are based on the OpenQASM standard for representing quantum operations. There is also a tutorial and online community. Quantum in the Cloud by The University of Bristol, which consists of a quantum simulator and a four qubit optical quantum system. Quantum Playground by Google is an educational resource which features a simulator with a simple interface, and a scripting language and 3D quantum state visualization. Quantum in the Cloud is an experimental quantum cloud platform for access to a four-qubit nuclear magnetic resonance-NMRCloudQ computer, managed by Tsinghua University. Quantum Inspire by Qutech is the first platform in Europe providing cloud-based quantum computing to two hardware chips. Next to a 5-qubit transmon processor, Quantum Inspire is the first platform in the world to provide online access to a fully programmable 2-qubit electron spin quantum processor. Amazon Braket is a cloud-based quantum computing platform hosted by AWS which, as of June 2025, provides access to quantum computers built by IonQ, Rigetti, IQM, and QuEra. Braket also provides a quantum algorithm development environment and simulator. Forge by QC Ware is a cloud-based quantum computing platform that provides access to D-Wave hardware, as well as Google and IBM simulators. The platform offers a 30-day free trial, including one minute of quantum computing time. Quantum-as-a-Service by Scaleway is a cloud-based platform created in 2022 to access to real quantum hardware from IQM Quantum Computers, Alpine Quantum Technologies, Quandela and Pasqal. It also include access to GPU-powered emulators such as Aer, Qsim and Quandela proprietary emulation.
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List of broadband over power line deployments

This is a list of broadband over power line deployments. In this sense, "broadband" usually refers to Internet access using power line communication technology. == BPL pilot projects - 1st Gen (UPA) == === Inactive pilot projects === North America: United States: The United Telecom Council publishes the Federal Communications Commission (FCC)-mandated BPL Interference Resolution website, which provides a list of all BPL deployments in the US. Canada: Quebec: As of 2005, PLC communication technology developed by Ariane Controls is being installed inside and outside existing buildings to control lights and other energy-hungry devices. The cheap devices allow energy consumption to be better managed, and so save much energy and bring a clear return on investment. Western Europe: Sweden: Vattenfall is using PLC technology at 1200 baud for automatic meter reading based on an Iskraemeco product. Central and Eastern Europe, and Eurasia: Russian Federation: Electro-com has deployed widely BPL/PLC technology and offers internet access service in Moscow, Nizhny Novgorod, Ryazan, Kaluga and Rostov-on-Don, planning to extend coverage to main Russian cities. Currently the company does not provide other services, though plans to start providing telephone, and television services someday. Base equipment is a DefiDev modem with a DS2 chipset. The company had 35,000 subscribers and an annual growth of 15-20%. The company has, however, halted operations in Moscow in September, 2008, having sold its client network to an IDSL internet provider. Romania: In January, 2006, the Ministry of Communications and Information Technology introduced a PLC trial in the rural locality of Band, Mureș County, offering phone and broadband internet access for €7 per month. The technology was introduced to 50 households. Montenegro: In March, 2002, the Internet Crna Gora biggest internet provider in Montenegro launched a pilot project in town of Cetinje. Serbia: In August 2002, the Star Engineering from Niš launched a pilot project to show a completely new way to access the Internet, which is a new in that time in most countries around the world. Hungary: The first powerline service in Hungary was realized in September, 2003, in the Riverside apartment house in Budapest by 23Vnet Ltd. The PLC equipment was supplied by ASCOM Powerline. After four months the service was counting 100 users from 450 apartment owners. The bandwidth is 4.5 Mbit/s. Asia, Pacific, and Oceania: Indonesia: PT Kejora Gemilang Internusa "KEJORA", under their banner PLANET BROADBAND, is currently rolling out broadband over power line, with over 300,000 homes expected to be enabled by August 2010. PT. Kejora Gemilang Internusa signed an 8-year Joint Venture concession agreement with ICON+ a division of PT. Perusahaan Listrik Negara (Indonesia electricity company). Under the terms of the agreement PLAnet Broadband are to supply BPL/PLC to Jakarta West and West Java. Another company, PT. Broadband Powerline Indonesia, has been developing broadband over power line in apartment buildings since 2006. PT. BPI also produces data couplers to make broadband over powerline possible in three phases (R, S, T) with a single master. India : In India IIIT Allahabad has completed a project in co-operation with Corinex Communications Canada to implement a prototype of BPL for University campus and nearby villages. Africa and the Middle East: Egypt: The Engineering Office for Integrated Projects (EOIP) has deployed PLC technology widely in Alexandria, Fayed, and Tanta. Based on a locally developed system, the company provides AMR for electricity utilities. Currently, the company has about 70,000 subscribers. South Africa: Goal Technology Solutions (GTS) trialled the technology and is offering service in the suburbs of Pretoria, and plans to extend it to other areas. The tests were done with Mitsubishi equipment using a DS2 chipset, and the company claims a maximum throughput of 90 Mbit/s although initially only "512 Kbits/s ADSL equivalent speeds" are available. Now it uses DefiDev's equipment, and according to GTS's website, it will expand available bandwidth up to 5-20 Mbit/s. Ghana: Cactel Communications, Ltd. successfully deployed an MV solution pilot project in the Graphic Communications Group in Accra in June, 2005. A Cactel Remote Energy Management System (REMS) pilot project for the Electricity Company of Ghana (ECG) is running a 40-user pilot project at the University of Ghana in Legon. The current project combines fiber, radio link, Wi-Fi and PLC to provide broadband internet access and telephony. It showcases the interoperability of PLC technology and the company's expertise in emerging market design and deployment. Cactel hopes to deploy nationally, and is in deliberations with the national stakeholders and with Ghana's Ministry of Communications (MoC). AllTerra Communications successfully implemented a pilot test of broadband over power lines in Akosombo. In partnership with VRA, this test involves demonstrating transmission of broadband from medium to low voltage signals. AllTerra is working with VRA to expand the pilot project to include essential grid management utilities that will help balance and manage the current electricity transmission throughout their various substations. Using IT as a catalyst for economic development, AllTerra is expanding into numerous areas throughout Ghana. Vobiss Solutions Ltd successfully implemented a Hybrid Fibre BPL pilot network within EMEFS Hillview Estate in collaboration with ECG. Saudi Arabia: ElectroNet has been working with the Saudi Electric Company since 2005 on a pilot project using broadband over power lines over medium voltage cables and linking into low voltage distribution within a shopping mall. The pilot project also integrates automatic meter readers. Powerlines Communications Co. Ltd. implemented an AMR pilot project for Saudi Electricity Company in 2006. The project was located in the city of Jeddah on the west coast of Saudi Arabia. Digital KWh meters were installed in parallel with analog KWh meters. Readings taken by the Saudi Electricity Company showed variations of less than 1%. A BPL pilot project was included. Saudi Arabian Computer Management Consultants (SACMAC) has signed a deal to become an official system integrator and distributor for Mitsubishi PLC. It is expected to become a great success, because the existing broadband service, monopolized by the Saudi Telecom Company, is expensive and has poor customer service (some clients report that company techs arrive months after ordering). SACMAC has declined to talk about specifics of availability and price but says it will start rolling out the service in a few months (as of May 2006) and its price will be lower than current broadband providers. === Concluded pilot projects === The following pilot projects have ended: Australia, Tasmania: In November 2007, electricity retailer Aurora Energy ended its involvement with BPL and announced it was switching to Optical Fiber. This ended their commercial trial begun in September 2005, offering BPL services to 500 homes in the suburb of Tolmans Hill near Hobart, which had followed a successful technological trial earlier that year. Portugal ended BPL/PLC deployments in the country in October 2006, reportedly for economic reasons., Russian Federation: In September 2008, Russia's only BPL provider Electro-com ended deployments in Moscow for economic reasons. Spain: In May 2007 Iberdrola and Endesa (the main power companies in Spain) ended their projects to deploy PLC. United States: As of July 2010, the City of Manassas, VA has shut down their BPL deployment, which was the largest in the country. As of April 2007, Motorola has shuttered its Powerline LV Access BPL and reportedly plans to re-purpose the technology to a new system called Powerline MU, which is for use within multiple-unit dwellings. Motorola's system uses only residential-side low-voltage power lines for transmission to reduce the antenna effect, and successfully demonstrated frequency-notching for reduced potential for interference over the Amperion Inc. and Current Technologies LLC systems. Motorola invited the American Radio Relay League to participate with these tests, and even installed the Motorola system at their headquarters. Preliminary results were very positive with regard to interference, because the Motorola system does not use BPL on the powerlines leading up to the neighborhood. The BPL carrier is only used for the last leg of the trip from the pole to the house, and gets the signal to the pole via radio. This limits the interference to the area surrounding the last leg to the house. === Dismantled pilot projects === The following other BPL trials in the US are dismantled as of May 2008:
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Social media background check

A social media background check is an investigative technique that involves scrutinizing the social media profiles and activities of individuals, primarily for pre-employment screening and other official verifications. These checks are performed to review people's online behavioral history on social media websites such as Facebook, Twitter, and LinkedIn. Social media background checks have become a common part of recruitment processes, among other verification procedures. == History == In the early 21st century, with the rapid expansion of social media platforms such as Facebook, Twitter, and LinkedIn, employers began to use these channels to gather additional information about prospective employees. Initially, social media background checks were an informal aspect of recruitment, but they have gradually gained formal recognition as a crucial element in candidate screening. Proponents of social media background checks argue that such reviews provide insight into a candidate's professional interests and networks, though the reliability of such assessments remains contested among researchers. == Rise in society == The practice of social media background checks has seen a significant surge in the last decade. This rise can be attributed to the exponential increase in social media users and the growing awareness among organizations regarding the importance of hiring individuals who align with their values and culture. Various platforms provide services explicitly designed to conduct social media background checks efficiently, simplifying the process for businesses. Companies providing social media background check services, such as Ferretly and Certn, have received venture capital funding, reflecting investor interest in the sector. The incorporation of artificial intelligence into conducting AI-powered social media background checks also illustrates its continued popularity and that businesses are looking to ramp up and even automate their use. High-profile cases in which individuals faced employment or admission consequences for past social media posts have raised awareness of social media background checking practices. For example, director James Gunn faced termination from Marvel Studios in 2018 over past offensive tweets, though he was later rehired. Additionally, multiple college admissions officers have acknowledged reviewing applicants' social media profiles, though such practices vary by institution. == Evolution of ethical considerations == Social media background checks are not without controversy, raising significant ethical considerations that have evolved in recent years. Privacy advocates argue that social media background checks raise concerns about data use and discrimination, particularly given the use of personal information that may not reflect job-relevant behavior. Legal scholars debate whether reviewing publicly posted information constitutes a privacy violation under U.S. law. Researchers and critics note that social media profiles often present curated representations of users' lives and may not reflect workplace behavior or professional competence. Moreover, the accuracy of social media background checks has been called into question, with critics pointing out that these checks may not always yield reliable or comprehensive results. Critics also warn about potential misuse of information obtained from social media, including cyberbullying and harassment. A 2023 study by found that approximately 90% of employers incorporate social media into hiring processes, with over half of those surveyed reporting they had rejected candidates based on social media content. This informal approach operates largely outside federal compliance frameworks. Critics argue that without regulation, candidates lack dispute mechanisms available under regulatory frameworks like the Fair Credit Reporting Act (FCRA), which requires compliance when background checks formally influence employment decisions. In a hiring environment where the practice is already performed often on an individual basis, the introduction of systematic, regulated screening practices that meet federal compliance standards can present a better, fairer alternative for both employers and candidates. == Business considerations == From a business perspective, social media background checks can be a valuable tool in protecting an organization's reputation and maintaining a safe and respectful workplace environment. A well-conducted social media background check can identify potential red flags, helping to prevent instances of workplace harassment or other negative behaviors. However, businesses also face potential legal repercussions if social media background checks are conducted improperly, such as non-compliance with the Fair Credit Reporting Act (FCRA) in the United States. Critics argue that over-reliance on social media data may exclude qualified candidates whose professional competence is not reflected in their online presence. The proliferation of social media screening services has prompted legal and industry experts to emphasize the importance of compliance with the Fair Credit Reporting Act and relevant state privacy laws when conducting such checks.
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Malleability (cryptography)

Malleability is a property of some cryptographic algorithms. An encryption algorithm is said to be malleable if it is possible to transform a ciphertext into another ciphertext which decrypts to a related plaintext. That is, given an encryption of a plaintext m {\displaystyle m} , it is possible to generate another ciphertext which decrypts to f ( m ) {\displaystyle f(m)} , for a known function f {\displaystyle f} , without necessarily knowing or learning m {\displaystyle m} . Malleability is often an undesirable property in a general-purpose cryptosystem, since it allows an attacker to modify the contents of a message. For example, suppose that a bank uses a stream cipher to hide its financial information, and a user sends an encrypted message containing, say, "TRANSFER $0000100.00 TO ACCOUNT #199." If an attacker can modify the message on the wire, and can guess the format of the unencrypted message, the attacker could change the amount of the transaction, or the recipient of the funds, e.g. "TRANSFER $0100000.00 TO ACCOUNT #227". Malleability does not refer to the attacker's ability to read the encrypted message. Both before and after tampering, the attacker cannot read the encrypted message. On the other hand, some cryptosystems are malleable by design. In other words, in some circumstances it may be viewed as a feature that anyone can transform an encryption of m {\displaystyle m} into a valid encryption of f ( m ) {\displaystyle f(m)} (for some restricted class of functions f {\displaystyle f} ) without necessarily learning m {\displaystyle m} . Such schemes are known as homomorphic encryption schemes. A cryptosystem may be semantically secure against chosen-plaintext attacks or even non-adaptive chosen-ciphertext attacks (CCA1) while still being malleable. However, security against adaptive chosen-ciphertext attacks (CCA2) is equivalent to non-malleability. == Example malleable cryptosystems == In a stream cipher, the ciphertext is produced by taking the exclusive or of the plaintext and a pseudorandom stream based on a secret key k {\displaystyle k} , as E ( m ) = m ⊕ S ( k ) {\displaystyle E(m)=m\oplus S(k)} . An adversary can construct an encryption of m ⊕ t {\displaystyle m\oplus t} for any t {\displaystyle t} , as E ( m ) ⊕ t = m ⊕ t ⊕ S ( k ) = E ( m ⊕ t ) {\displaystyle E(m)\oplus t=m\oplus t\oplus S(k)=E(m\oplus t)} . In the RSA cryptosystem, a plaintext m {\displaystyle m} is encrypted as E ( m ) = m e mod n {\displaystyle E(m)=m^{e}{\bmod {n}}} , where ( e , n ) {\displaystyle (e,n)} is the public key. Given such a ciphertext, an adversary can construct an encryption of m t {\displaystyle mt} for any t {\displaystyle t} , as E ( m ) ⋅ t e mod n = ( m t ) e mod n = E ( m t ) {\textstyle E(m)\cdot t^{e}{\bmod {n}}=(mt)^{e}{\bmod {n}}=E(mt)} . For this reason, RSA is commonly used together with padding methods such as OAEP or PKCS1. In the ElGamal cryptosystem, a plaintext m {\displaystyle m} is encrypted as E ( m ) = ( g b , m A b ) {\displaystyle E(m)=(g^{b},mA^{b})} , where ( g , A ) {\displaystyle (g,A)} is the public key. Given such a ciphertext ( c 1 , c 2 ) {\displaystyle (c_{1},c_{2})} , an adversary can compute ( c 1 , t ⋅ c 2 ) {\displaystyle (c_{1},t\cdot c_{2})} , which is a valid encryption of t m {\displaystyle tm} , for any t {\displaystyle t} . In contrast, the Cramer-Shoup system (which is based on ElGamal) is not malleable. In the Paillier, ElGamal, and RSA cryptosystems, it is also possible to combine several ciphertexts together in a useful way to produce a related ciphertext. In Paillier, given only the public key and an encryption of m 1 {\displaystyle m_{1}} and m 2 {\displaystyle m_{2}} , one can compute a valid encryption of their sum m 1 + m 2 {\displaystyle m_{1}+m_{2}} . In ElGamal and in RSA, one can combine encryptions of m 1 {\displaystyle m_{1}} and m 2 {\displaystyle m_{2}} to obtain a valid encryption of their product m 1 m 2 {\displaystyle m_{1}m_{2}} . Block ciphers in the cipher block chaining mode of operation, for example, are partly malleable: flipping a bit in a ciphertext block will completely mangle the plaintext it decrypts to, but will result in the same bit being flipped in the plaintext of the next block. This allows an attacker to 'sacrifice' one block of plaintext in order to change some data in the next one, possibly managing to maliciously alter the message. This is essentially the core idea of the padding oracle attack on CBC, which allows the attacker to decrypt almost an entire ciphertext without knowing the key. For this and many other reasons, a message authentication code is required to guard against any method of tampering. == Complete non-malleability == Fischlin, in 2005, defined the notion of complete non-malleability as the ability of the system to remain non-malleable while giving the adversary additional power to choose a new public key which could be a function of the original public key. In other words, the adversary shouldn't be able to come up with a ciphertext whose underlying plaintext is related to the original message through a relation that also takes public keys into account.
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Outline of automation

The following outline is provided as an overview of and topical guide to automation: Automation – use of control systems and information technologies to reduce the need for human work in the production of goods and services. In the scope of industrialization, automation is a step beyond mechanization. == Essence of automation == Control system – a device, or set of devices to manage, command, direct or regulate the behavior of other devices or systems. Industrial control system (ICS) – encompasses several types of control systems used in industrial production, including supervisory control and data acquisition (SCADA) systems, distributed control systems (DCS), and other smaller control system configurations such as skid-mounted programmable logic controllers (PLC) often found in industrial sectors and critical infrastructures. Industrialization – period of social and economic change that transforms a human group from an agrarian society into an industrial one. Numerical control (NC) – refers to the automation of machine tools that are operated by abstractly programmed commands encoded on a storage medium, as opposed to controlled manually via handwheels or levers, or mechanically automated via cams alone. Robotics – the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots and computer systems for their control, sensory feedback, and information processing. == Branches of automation == === General purpose === Autonomous automation – autonomous software agents to adapt the controllers of computer controlled industrial machinery and processes Banking automation Broadcast automation Building automation – advanced functionality provided by the control system of a building. A building automation system (BAS) is an example of a distributed control system. Home automation – control system of a home. Office automation – the varied computer machinery and software used to digitally create, collect, store, manipulate, and relay office information needed for accomplishing basic tasks such as business process automation and robotic process automation. Console automation Database automation Integrated library system Laboratory automation === Specific purpose === Automated attendant Automated guided vehicle Autonomous mobile robot Automated highway system Automated pool cleaner Automated teller machine Automatic painting (robotic) Pop music automation Remotely operated vehicle Robotic lawn mower Telephone switchboard Vending machine == Fields contributing to automation == Cybernetics – the interdisciplinary study of the structure of regulatory systems. Cognitive science – interdisciplinary scientific study of the mind and its processes. It examines what cognition is, what it does and how it works. Robotics – the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots and computer systems for their control, sensory feedback, and information processing. == History of automation == History of mass production – Prerequisites of mass production were interchangeable parts, machine tools and power, especially in the form of electricity. Mass production was popularized in the 1910s and 1920s by Henry Ford's Ford Motor Company, which introduced electric motors to the then-well-known technique of chain or sequential production. History of home automation == Automated machines == Machine to Machine OLE for process control (OPC) Process control – a statistics and engineering discipline that deals with architectures, mechanisms and algorithms for maintaining the output of a specific process within a desired range. Run Book Automation (RBA) Robot – a mechanical or virtual intelligent agent that can perform tasks automatically or with guidance, typically by remote control. == Automated machine components == Artificial intelligence – the intelligence of machines and the branch of computer science that aims to create it. Friendly artificial intelligence – an artificial intelligence that has a positive rather than negative effect on humanity, and the field of knowledge required to build such an artificial intelligence. === Automation tools === Artificial neural network (ANN) – mathematical model or computational model that is inspired by the structure or functional aspects of biological neural networks. Human machine interface (HMI) – operator level local control panel that monitors field devices Laboratory information management system (LIMS) – software package that offers a set of key features that support a modern laboratory's operations. Industrial control system – encompasses several types of control systems used in industrial production, including supervisory control and data acquisition (SCADA) systems, distributed control systems (DCS), and other smaller control system configurations such as skid-mounted programmable logic controllers (PLC) often found in the industrial sectors and critical infrastructures. Distributed control system (DCS) – control system usually of a manufacturing system, process or any kind of dynamic system, in which the controller elements are not central in location (like the brain) but are distributed throughout the system with each component sub-system controlled by one or more controllers. Manufacturing execution system (MES) – system that manages manufacturing operations in a factory, including management of resources, scheduling production processes, dispatching production orders, execution of production orders, etc. Programmable automation controller (PAC) – digital computer used for automation of electromechanical processes, such as control of machinery on factory assembly lines, amusement rides, or light fixtures. Programmable logic controller (PLC)A Programmable Logic Controller, PLC or Programmable Controller is a digital computer used for automation of electromechanical processes, such as control of machinery on factory assembly lines, amusement rides, or light fixtures. The abbreviation "PLC" and the term "Programmable Logic Controller" are registered trademarks of the Allen-Bradley Company (Rockwell Automation). PLCs are used in many industries and machines. Unlike general-purpose computers, the PLC is designed for multiple inputs and output arrangements, extended temperature ranges, immunity to electrical noise, and resistance to vibration and impact. Programs to control machine operation are typically stored in battery-backed-up or non-volatile memory. A PLC is an example of a hard real time system since output results must be produced in response to input conditions within a limited time, otherwise unintended operation will result. Supervisory control and data acquisition (SCADA) – generally refers to industrial control systems (ICS): computer systems that monitor and control industrial, infrastructure, or facility-based processes, as described below: Industrial processes include those of manufacturing, production, power generation, fabrication, and refining, and may run in continuous, batch, repetitive, or discrete modes. Simulation § Engineering Technology simulation or Process simulation == Social movements == Automation-related social movement – a movement that advocates semi- or fully automatic systems to provide for human needs globally. For example, automation of farming and food distribution throughout the world so that no one will go hungry. One goal is to automate all mundane labor, to free humans to engage in more creative activities (or less work). The Technocracy movement – social movement active from the Great Depression (1930s) to date that proposes replacing politicians and business people with scientists and engineers who have the technical expertise to manage the economy. The Zeitgeist Movement – movement advocating the replacement of the market economy with an economy in which all resources are equitably, commonly and sustainably shared. == Automation in the future == Android – a robot or synthetic organism designed to look and act like a human, and with a body having a flesh-like resemblance Technological singularity – the hypothetical future emergence of greater-than-human intelligence through technological means Semi-automation – using a centralized computer controller to orchestrate the activities of man and machine. == Automation-related publications == IEEE Spectrum – the flagship publication of the Institute of Electrical and Electronics Engineers (IEEE), explores the development, applications and implications of new technologies, and provides a forum for understanding, discussion and leadership in these areas. IEEE Transactions on Information Theory – peer-reviewed scientific journal published by the Institute of Electrical and Electronics Engineers (IEEE), focused on the study of information theory, the mathematics of communications, including computer communications, robotics communications, etc. IEEE Transactions on Control S
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Cut, copy, and paste

Cut, copy, and paste are essential commands of modern human–computer interaction and user interface design. They offer an interprocess communication technique for transferring data through a computer's user interface. The cut command removes the selected data from its original position, and the copy command creates a duplicate; in both cases the selected data is kept in temporary storage called the clipboard. Clipboard data is later inserted wherever a paste command is issued. The data remains available to any application supporting the feature, thus allowing easy data transfer between applications. The command names are a (skeuomorphic) interface metaphor based on the physical procedure used in manuscript print editing to create a page layout, like with paper. The commands were pioneered into computing by Xerox PARC in 1974, popularized by Apple Computer in the 1983 Lisa workstation and the 1984 Macintosh computer, and in a few home computer applications such as the 1984 word processor Cut & Paste. This interaction technique has close associations with related techniques in graphical user interfaces (GUIs) that use pointing devices such as a computer mouse (by drag and drop, for example). Typically, clipboard support is provided by an operating system as part of its GUI and widget toolkit. The capability to replicate information with ease, changing it between contexts and applications, involves privacy concerns because of the risks of disclosure when handling sensitive information. Terms like cloning, copy forward, carry forward, or re-use refer to the dissemination of such information through documents, and may be subject to regulation by administrative bodies. == History == === Origins === The term "cut and paste" comes from the traditional practice in manuscript editing, whereby people cut paragraphs from a page with scissors and paste them onto another page. This practice remained standard into the 1980s. Stationery stores sold "editing scissors" with blades long enough to cut an 8½"-wide page. The advent of photocopiers made the practice easier and more flexible. The act of copying or transferring text from one part of a computer-based document ("buffer") to a different location within the same or different computer-based document was a part of the earliest on-line computer editors. As soon as computer data entry moved from punch-cards to online files (in the mid/late 1960s) there were "commands" for accomplishing this operation. This mechanism was often used to transfer frequently-used commands or text snippets from additional buffers into the document, as was the case with the QED text editor. === Early methods === The earliest editors (designed for teleprinter terminals) provided keyboard commands to delineate a contiguous region of text, then delete or move it. Since moving a region of text requires first removing it from its initial location and then inserting it into its new location, various schemes had to be invented to allow for this multi-step process to be specified by the user. Often this was done with a "move" command, but some text editors required that the text be first put into some temporary location for later retrieval/placement. In 1983, the Apple Lisa became the first text editing system to call that temporary location "the clipboard". Earlier control schemes such as NLS used a verb—object command structure, where the command name was provided first and the object to be copied or moved was second. The inversion from verb—object to object—verb on which copy and paste are based, where the user selects the object to be operated before initiating the operation, was an innovation crucial for the success of the desktop metaphor as it allowed copy and move operations based on direct manipulation. === Popularization === Inspired by early line and character editors, such as Pentti Kanerva's TV-Edit, that broke a move or copy operation into two steps—between which the user could invoke a preparatory action such as navigation—Lawrence G. "Larry" Tesler proposed the names "cut" and "copy" for the first step and "paste" for the second step. Beginning in 1974, he and colleagues at Xerox PARC implemented several text editors that used cut/copy-and-paste commands to move and copy text. Apple Computer popularized this paradigm with its Lisa (1983) and Macintosh (1984) operating systems and applications. The functions were mapped to key combinations using the ⌘ Command key as a special modifier, which is held down while also pressing X for cut, C for copy, or V for paste. These few keyboard shortcuts allow the user to perform all the basic editing operations, and the keys are clustered at the left end of the bottom row of the standard QWERTY keyboard. These are the standard shortcuts: Control-Z (or ⌘ Command+Z) to undo Control-X (or ⌘ Command+X) to cut Control-C (or ⌘ Command+C) to copy Control-V (or ⌘ Command+V) to paste The IBM Common User Access (CUA) standard also uses combinations of the Insert, Del, Shift and Control keys. Early versions of Windows used the IBM standard. Microsoft later also adopted the Apple key combinations with the introduction of Windows, using the control key as modifier key. Similar patterns of key combinations, later borrowed by others, are widely available in most GUI applications. The original cut, copy, and paste workflow, as implemented at PARC, utilizes a unique workflow: With two windows on the same screen, the user could use the mouse to pick a point at which to make an insertion in one window (or a segment of text to replace). Then, by holding shift and selecting the copy source elsewhere on the same screen, the copy would be made as soon as the shift was released. Similarly, holding shift and control would copy and cut (delete) the source. This workflow requires many fewer keystrokes/mouse clicks than the current multi-step workflows, and did not require an explicit copy buffer. It was dropped, one presumes, because the original Apple and IBM GUIs were not high enough density to permit multiple windows, as were the PARC machines, and so multiple simultaneous windows were rarely used. == Cut and paste == Computer-based editing can involve very frequent use of cut-and-paste operations. Most software-suppliers provide several methods for performing such tasks, and this can involve (for example) key combinations, pulldown menus, pop-up menus, or toolbar buttons. The user selects or "highlights" the text or file for moving by some method, typically by dragging over the text or file name with the pointing-device or holding down the Shift key while using the arrow keys to move the text cursor. The user performs a "cut" operation via key combination Ctrl+x (⌘+x for Macintosh users), menu, or other means. Visibly, "cut" text immediately disappears from its location. "Cut" files typically change color to indicate that they will be moved. Conceptually, the text has now moved to a location often called the clipboard. The clipboard typically remains invisible. On most systems only one clipboard location exists, hence another cut or copy operation overwrites the previously stored information. Many UNIX text-editors provide multiple clipboard entries, as do some Macintosh programs such as Clipboard Master, and Windows clipboard-manager programs such as the one in Microsoft Office. The user selects a location for insertion by some method, typically by clicking at the desired insertion point. A paste operation takes place which visibly inserts the clipboard text at the insertion point. (The paste operation does not typically destroy the clipboard text: it remains available in the clipboard and the user can insert additional copies at other points). Whereas cut-and-paste often takes place with a mouse-equivalent in Windows-like GUI environments, it may also occur entirely from the keyboard, especially in UNIX text editors, such as Pico or vi. Cutting and pasting without a mouse can involve a selection (for which Ctrl+x is pressed in most graphical systems) or the entire current line, but it may also involve text after the cursor until the end of the line and other more sophisticated operations. The clipboard usually stays invisible, because the operations of cutting and pasting, while actually independent, usually take place in quick succession, and the user (usually) needs no assistance in understanding the operation or maintaining mental context. Some application programs provide a means of viewing, or sometimes even editing, the data on the clipboard. == Copy and paste == The term "copy-and-paste" refers to the popular, simple method of reproducing text or other data from a source to a destination. It differs from cut and paste in that the original source text or data does not get deleted or removed. The popularity of this method stems from its simplicity and the ease with which users can move data between various applications visually – without resorting to permanent storage. Use in healthcare do
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Data marketplace

Data marketplace is an online platform for sharing and consuming data in the form of data assets or data products. Part of the data management stack, it aims to bring together data producers and data consumers (including business users and AI) in a single space, with the objective of increasing access to understandable, high-quality data. Included within its Data Marketplaces and Exchange (DME) category by Gartner, data marketplaces can provide data internally within an organization, externally with partners, or as open data. == Concept == Digitization has dramatically increased data volumes within organizations, with IDC predicting that by 2025 the world will contain 175 zettabytes of data. This has created a need to both manage this data and provide access to it to enable business intelligence and data analysis. However, data is often scattered within multiple systems (such as data warehouses and data lakes), and is in formats that are only understandable by technical experts, such as data scientists. According to IDC, 81% of IT leaders cite data silos as a major barrier to digital transformation. This means that data is not freely available to business users or external audiences such as partners or citizens, limiting its value, and holding back AI deployments. Data marketplaces solve this issue, providing seamless, self-service access to high-quality data in an understandable, secure and auditable manner. They break down data silos, reduce friction in data access, and enable a broader range of users, including non-technical profiles, to find, understand, and consume data autonomously. Data assets on the marketplace can be raw data, data visualizations or data products. Data marketplaces combine data management functions such as data governance with the user-friendly experience offered by e-commerce marketplaces in order to increase the usage of data. These include features such as powerful search engines, feedback, ratings, subscriptions and product description sheets. According to Gartner, data marketplaces provide infrastructure, transactional capabilities, and services for both consumers and providers of data assets. == History and timeline == Data marketplaces have evolved since they first emerged in terms of both their scope and usage. === 2000s === With the rise of the internet, data brokers began collecting, aggregating, distributing and selling personal, financial and marketing data to third parties online. Data marketplaces were deployed to monetize this data, making it discoverable and accessible to users, either through subscriptions or one-off purchases. At the same time, regulations, such as the US Open Government Initiative of 2009 and others around the world mandated greater transparency and data sharing with the public. Data sharing portals were created by public and government bodies to make this information available through self-service to all users. === 2010s === Due to the growth of big data and cloud platforms, cloud-based data exchange platforms emerged. These were offered by major infrastructure providers, and included Amazon Web Services (AWS) Data Exchange, Snowflake Data Marketplace, and the Google Cloud Platform. These platforms moved beyond simple data brokerage or open data by providing structured, catalogued data sharing between organizations. === 2020s === Driven by a need to increase internal data sharing with both business users and AI, organizations are now looking to adopt internal data marketplaces. These aim to democratize data consumption by providing seamless access for all employees and AI to trusted data, including data products, through an intuitive, e-commerce style experience. According to Gartner analyst Richa Jha, "by providing a single, governed platform for discovering, sharing, and scaling data products, data marketplaces drive productivity, collaboration, and ROI across the enterprise." == Data marketplaces within the overall data architecture == Data marketplaces provide a consumption and collaboration layer for data. That means they complement and integrate with other parts of the overall data architecture, including: === Data warehouses and data lakes === Data marketplaces connect to data sources, such as data warehouses or data lakes, to provide intuitive access to the data stored within them, enabling data to be shared and distributed to non-technical audiences. Access can be direct, with data and data products stored within the data marketplace or virtualized. === Data catalog === A data catalog provides a technical inventory of an organization's data estate. It collects technical information on all available data assets within an organization, based on metadata descriptions. This ensures traceability, and supports compliance and governance requirements. Unlike a data marketplace, a data catalog does not provide access to data, and is designed to be used by data professionals, rather than the business. This means it lacks an intuitive, understandable interface and is consequently not easily accessible by business users. === Data mesh === Data mesh is an architecture and framework for data management, first defined by Zhamak Dehghani in 2019. It aims to decentralize data ownership to delegate responsibility, empowering teams and focusing on delivering data to users in the form of self-service data products. The data marketplace is a central pillar of data mesh, providing intuitive access to these data products, and creating a collaboration space for data owners and data consumers. === Data product === Data products are high-value, consumable data assets that package high-quality data and associated tools to enable seamless usage by business users at scale. First defined by McKinsey in 2022, they have an identified owner, a service level agreement (SLA), and a reusability logic. == Core components of a data marketplace == A data marketplace typically includes specific core components: === E-commerce style interface === An e-commerce style experience that engages non-technical users, minimizes the need for training and builds confidence and trust in data. Look and feel should be customizable to incorporate corporate design guidelines to ensure consistency with other organizational applications. === Built-in data catalog === As in a standalone data catalog, this indexes all available data, based on metadata that includes type, source, owner, freshness, and quality level. === Discovery and search engine === This enables users to search, filter, explore and discover available data intuitively. As in an e-commerce marketplace, it should be intelligent, and provide relevant results based on natural language queries. === Access control and security management === Data marketplaces will contain data that needs to be protected under regulations such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and sector-specific frameworks in industries such as finance and healthcare. To ensure both security and compliance while maximizing data consumption, the data marketplace should include granular access management and a full audit trail. === Semantic layer and business glossary === Different parts of the business are likely to use different terms to describe data. This leads to inconsistencies and an inability to share data across systems and teams. The semantic layer and business glossary standardize a shared vocabulary and common definitions of business indicators and concepts, providing a single language for data across the business and for AI agents. === Data governance mechanisms === These enforce corporate data governance policies, ensuring data traceability through data lineage, quality certification, usage monitoring, and continuous improvement through user feedback loops. === Collaboration features === As on an e-commerce website, a data marketplace should provide collaboration features that bring together data users and data owners. This includes the ability to rate data products, share use cases, and provide feedback to data owners, creating a community around data and supporting a data-driven culture. == Types of data marketplace == While they share the same underlying technology, data marketplaces can be deployed in three broad ways: === Internal data marketplaces === These bring together data from across an organization and make it available via self-service to employees from across the business. They aim to widen access to data and consequently to improve decision-making and reporting, increase performance and maximize efficiency. === Ecosystem data marketplaces === These extend sharing beyond a single organization, enabling multiple partners (public institutions, industry players, research bodies) to share and consume data within a governed framework. Data can be provided by all parties or simply by one organization and consumed by others. Ecosystem data marketplaces are particularly relevant in
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