AI Data Operations

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

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  Dave's Redistricting

  Dave's Redistricting App (DRA) is an online web app originally created by Dave Bradlee that allows anyone to simulate redistricting a U.S. state's congressional and legislative districts. == Purpose == According to Bradlee, the software was designed to "put power in people's hands," and so that they "can see how the process works, so it's a little less mysterious than it was 10 years ago." Bradlee has noticed that many citizens are taking this process seriously and using his app to create legitimate redistricting maps that could be put in place. Some websites have called Bradlee the pioneer and cause of the rise of do-it-yourself redistricting. States such as Montana in 2021 allowed the general population to use it to submit redistricting proposals following the 2020 United States Census. Dave's Redistricting has frequently been mentioned as a resource that can be used to combat gerrymandering, given that the public has free access to it. Political science firms such as FiveThirtyEight have used the website to draw examples of gerrymandered districts, including on their famous Atlas of Redistricting. Dave Bradlee built the first generation of DRA. DRA 2020 is built by a small team of volunteers—Dave Bradlee, Terry Crowley, Alec Ramsay, and David Rinn—all with a shared passion for technology & democracy and all Microsoft veterans. Their mission is to empower civic organizations and citizen activists to advocate for fair congressional and legislative districts and increased transparency in the redistricting process. == Functions == Users can redraw the congressional and state legislative districts for all 50 states, the District of Columbia, and Puerto Rico using a variety of census and election datasets including Cook PVI. Maps can be optimized for different criteria. DRA 2020 added several major features to the first generation app: Sharing & collaborative editing of maps, like Google Docs Multiple statewide elections for all 50 states including the ability to import your own data Comprehensive analytics for evaluating and comparing maps Custom overlays, and Block-level editing DRA remains free to use. == Versions == 2.2: This uses Bing Maps, an outdated software that projects the districts of a single state onto a map of the United States. 2.5: After Bing Maps announced that it would no longer be updating for the foreseen future, the U.S. Map feature was removed. DRA 2020: At the end of 2018, a beta version of 2020 was released. This version that did not require Microsoft Silverlight and could be used in any web browser. DRA 2020 has been under continuous development since and is built using React (JavaScript library), Mapbox, OpenStreetMap, TypeScript, Node.js, Amazon Web Services, as well as many open source components, tools, and icons.

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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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  Social bot

  A social bot, refers to fully or partially automated social media accounts designed to perform most regular users’ actions, such as liking, posting content, and chatting with other users. Although their levels of autonomy vary, and often include a human-in-the-loop, social bots can use artificial intelligence to perform social media actions and can use large language models to mimic human dialogue. Social bots can operate alone or in groups that coordinate messaging as part of a network of coordinated inauthentic behavior. Social bots are often used to perform ad fraud by artificially boosting viewership and engagement metrics and to spread disinformation on social media. == Uses == Social bots are used for a large number of purposes on a variety of social media platforms, including Twitter, Instagram, Facebook, and YouTube. One common use of social bots is to inflate a social media user's apparent popularity, usually by artificially manipulating their engagement metrics with large volumes of fake likes, reposts, or replies. Social bots can similarly be used to artificially inflate a user's follower count with fake followers, creating a false perception of a larger and more influential online following than is the case. The use of social bots to create the impression of a large social media influence allows individuals, brands, and organizations to attract a higher number of human followers and boost their online presence. Fake engagement can be bought and sold in the black market of social media engagement. Corporations typically use automated customer service agents on social media to affordably manage high levels of support requests. Social bots are used to send automated responses to users’ questions, sometimes prompting the user to private message the support account with additional information. The increased use of automated support bots and virtual assistants has led to some companies laying off customer-service staff. Social bots are also often used to influence public opinion. Autonomous bot accounts can flood social media with large numbers of posts expressing support for certain products, companies, or political campaigns, creating the impression of organic grassroots support. This can create a false perception of the number of people who support a certain position, which may also have effects on the direction of stock prices or on elections. Messages with similar content can also influence fads or trends. Many social bots are also used to amplify phishing attacks. These malicious bots are used to trick a social media user into giving up their passwords or other personal data. This is usually accomplished by posting links claiming to direct users to news articles that would in actuality direct to malicious websites containing malware. Scammers often use URL shortening services such as TinyURL and bit.ly to disguise a link's domain address, increasing the likelihood of a user clicking the malicious link. The presence of fake social media followers and high levels of engagement help convince the victim that the scammer is in fact a trusted user. Social bots can be a tool for computational propaganda. Bots can also be used for algorithmic curation, algorithmic radicalization, and/or influence-for-hire, a term that refers to the selling of an account on social media platforms. == History == Bots have coexisted with computer technology since the earliest days of computing. Social bots have their roots in the 1950s with Alan Turing, whose work focused on machine intelligence with the development of the Turing Test. The following decades saw further progress made towards the goal of creating programs capable of mimicking human behavior, notably with Joseph Weizenbaum’s creation of ELIZA. Considered to be one of the first Chatbots, ELIZA could simulate natural conversations with human users through pattern matching. Its most famous script was DOCTOR, a simulation of a Rogerian psychotherapist that was programmed to chat with patients and respond to questions. With the growth of social media platforms in the early 2000s, these bots could be used to interact with much larger user groups in an inconspicuous manner. Early instances of autonomous agents on social media could be found on sites like MySpace, with social bots being used by marketing firms to inflate activity on a user’s page in an effort to make them appear more popular. Social bots have been observed on a large variety of social media websites, with Twitter being one of the most widely observed examples. The creation of Twitter bots is generally against the site’s terms of service when used to post spam or to automatically like and follow other users, but some degree of automation using Twitter’s API may be permitted if used for “entertainment, informational, or novelty purposes.” Other platforms such as Reddit and Discord also allow for the use of social bots as long as they are not used to violate policies regarding harmful content and abusive behavior. Social media platforms have developed their own automated tools to filter out messages that come from bots, although they cannot detect all bot messages. == Legal regulation == Due to the difficulty of recognizing social bots and separating them from "eligible" automation via social media APIs, it is unclear how legal regulation can be enforced. Social bots are expected to play a role in shaping public opinion by autonomously acting as influencers. Some social bots have been used to rapidly spread misinformation, manipulate stock markets, influence opinion on companies and brands, promote political campaigns, and engage in malicious phishing campaigns. In the United States, some states have started to implement legislation in an attempt to regulate the use of social bots. In 2019, California passed the Bolstering Online Transparency Act (the B.O.T. Act) to make it unlawful to use automated software to appear indistinguishable from humans for the purpose of influencing a social media user's purchasing and voting decisions. Other states such as Utah and Colorado have passed similar bills to restrict the use of social bots. The Artificial Intelligence Act (AI Act) in the European Union is the first comprehensive law governing the use of Artificial Intelligence. The law requires transparency in AI to prevent users from being tricked into believing they are communicating with another human. AI-generated content on social media must be clearly marked as such, preventing social bots from using AI in a manner that mimics human behavior. == Detection == The first generation of bots could sometimes be distinguished from real users by their often superhuman capacities to post messages. Later developments have succeeded in imprinting more "human" activity and behavioral patterns in the agent. With enough bots, it might be even possible to achieve artificial social proof. To unambiguously detect social bots as what they are, a variety of criteria must be applied together using pattern detection techniques, some of which are: cartoon figures as user pictures sometimes also random real user pictures are captured (identity fraud) reposting rate temporal patterns sentiment expression followers-to-friends ratio length of user names variability in (re)posted messages engagement rate (like/followers rate) analysis of the time series of social media posts Social bots are always becoming increasingly difficult to detect and understand. The bots' human-like behavior, ever-changing behavior of the bots, and the sheer volume of bots covering every platform may have been a factor in the challenges of removing them. Social media sites, like Twitter, are among the most affected, with CNBC reporting up to 48 million of the 319 million users (roughly 15%) were bots in 2017. Botometer (formerly BotOrNot) is a public Web service that checks the activity of a Twitter account and gives it a score based on how likely the account is to be a bot. The system leverages over a thousand features. An active method for detecting early spam bots was to set up honeypot accounts that post nonsensical content, which may get reposted (retweeted) by the bots. However, bots evolve quickly, and detection methods have to be updated constantly, because otherwise they may get useless after a few years. One method is the use of Benford's Law for predicting the frequency distribution of significant leading digits to detect malicious bots online. This study was first introduced at the University of Pretoria in 2020. Another method is artificial-intelligence-driven detection. Some of the sub-categories of this type of detection would be active learning loop flow, feature engineering, unsupervised learning, supervised learning, and correlation discovery. Some operations of bots work together in a synchronized way. For example, ISIS used Twitter to amplify its Islamic content by numerous orchestrated accounts which further pushed an item to the Hot List news, thus further a

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  Cryptosystem

  In cryptography, a cryptosystem is a suite of cryptographic algorithms needed to implement a particular security service, such as confidentiality (encryption). Typically, a cryptosystem consists of three algorithms: one for key generation, one for encryption, and one for decryption. The term cipher (sometimes cypher) is often used to refer to a pair of algorithms, one for encryption and one for decryption. Therefore, the term cryptosystem is most often used when the key generation algorithm is important. For this reason, the term cryptosystem is commonly used to refer to public key techniques; however both "cipher" and "cryptosystem" are used for symmetric key techniques. == Formal definition == Mathematically, a cryptosystem or encryption scheme can be defined as a tuple ( P , C , K , E , D ) {\displaystyle ({\mathcal {P}},{\mathcal {C}},{\mathcal {K}},{\mathcal {E}},{\mathcal {D}})} with the following properties. P {\displaystyle {\mathcal {P}}} is a set called the "plaintext space". Its elements are called plaintexts. C {\displaystyle {\mathcal {C}}} is a set called the "ciphertext space". Its elements are called ciphertexts. K {\displaystyle {\mathcal {K}}} is a set called the "key space". Its elements are called keys. E = { E k : k ∈ K } {\displaystyle {\mathcal {E}}=\{E_{k}:k\in {\mathcal {K}}\}} is a set of functions E k : P → C {\displaystyle E_{k}:{\mathcal {P}}\rightarrow {\mathcal {C}}} . Its elements are called "encryption functions". D = { D k : k ∈ K } {\displaystyle {\mathcal {D}}=\{D_{k}:k\in {\mathcal {K}}\}} is a set of functions D k : C → P {\displaystyle D_{k}:{\mathcal {C}}\rightarrow {\mathcal {P}}} . Its elements are called "decryption functions". For each e ∈ K {\displaystyle e\in {\mathcal {K}}} , there is d ∈ K {\displaystyle d\in {\mathcal {K}}} such that D d ( E e ( p ) ) = p {\displaystyle D_{d}(E_{e}(p))=p} for all p ∈ P {\displaystyle p\in {\mathcal {P}}} . Note; typically this definition is modified in order to distinguish an encryption scheme as being either a symmetric-key or public-key type of cryptosystem. == Examples == A classical example of a cryptosystem is the Caesar cipher. A more contemporary example is the RSA cryptosystem. Another example of a cryptosystem is the Advanced Encryption Standard (AES). AES is a widely used symmetric encryption algorithm that has become the standard for securing data in various applications. Paillier cryptosystem is another example used to preserve and maintain privacy and sensitive information. It is featured in electronic voting, electronic lotteries and electronic auctions.

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  Sensory, Inc.

  Sensory, Inc. is an American company which develops software AI technologies for speech, sound and vision. It is based in Santa Clara, California. Sensory’s technologies have shipped in over three billion products from hundreds of leading consumer electronics manufacturers including AT&T, Hasbro, Huawei, Google, Amazon, Samsung, LG, Mattel, Motorola, Plantronics, GoPro, Sony, Tencent, Garmin, LG, Microsoft, Lenovo, and more. Sensory has over 60 issued patents covering speech recognition in consumer electronics, biometric authentication, sensor/speech combinations, wake word technology, and more. == History == Sensory, Inc. was founded in 1994, originally as Sensory Circuits, by Forrest Mozer, Mike Mozer and Todd Mozer. The three had also co-founded ESS Technology years earlier. In 1999 Sensory acquired Fluent Speech Technologies, which was formed and started by a group of professors out of the Oregon Graduate Institute (formerly OGI, now OHSU). Fluent Speech Technologies developed high performance embedded speech engines, the technology from this acquisition is now the core technology used throughout Sensory's chip and software line. === Company timeline === 1994 – Founded 1995 – Introduces the RSC 164 - first commercially successful speech recognition IC 1998 – Introduces first speaker verification IC 2000 – Acquires Oregon based Fluent-Speech Technologies 2002 – Acquires Texas Instruments line of speech output ICs (the SC series) 2007 – Introduces first Voice User Interface for Bluetooth silicon (CSR BC-5) - BlueGenie 2008 - Sensory and BlueAnt partner on the V1 - Revolutionary new Bluetooth headset with a voice user interface. First wearable to use a voice user interface for control and best-reviewed speech recognition product in history 2009 – Introduced world's smallest text to speech system (TTS) and Truly HandsfreeTM Triggers/ wake words. 2010 – Introduced the NLP-5x – First Natural Language Voice Processor and TrulyHandsfree wake words in SDKs for Android, iOS, Linux, and Windows. NLP5x used the first generation of TrulyHandsfree wake words with low power and enhanced accuracy. 2011 – Sensory partners with Google and Microsoft to enable TrulyHandsfree as a front end to Goog411 and Bing411 2012 – Partnered with Tensilica to offer ultra-low power TrulyHandsfree wake words; introduced Speaker Verification and Speaker Identification for mobile phones and other consumer electronics. 2012 - TrulyHandsfree released into Samsung's Galaxy S2 for "Hey Galaxy" wake word 2013 – TrulyHandsfree wake words migrated to many new platforms and began shipping as MotoVoice in the Google-owned MotoX. Sensory's TrulyHandsfree in mobile takes off with the Galaxy S3 and S4 and Galaxy Note and is licensed into wearables like Google Glass. 2014 – Announced new initiative in Vision; added LG and Motorola as customers; received the 2014 Global Mobile Award for Best Mobile Technology Breakthrough at the GSMA Mobile World Congress in Barcelona, Spain (judges commented, "A big advance for the wearables market, this offers many benefits for consumers, increasing uptake and usage of many mobile apps, driving revenue for operators and content providers.") 2015-2018 - Licensed Google, Amazon, MSFT, Baidu, Huawei, ZTE, and many others with TrulyHandsfree wake words. Sensory develops first wake words for OK Google, Hey Siri, and Hey Cortana. 2019 - Sensory launched two new solutions: SoundID, sound identification, and TrulyNatural, embedded large vocabulary speech recognition. Sensory also acquired Vocalize.ai, an independent testing lab. 2020 - Sensory introduced VoiceHub, which allows the automated generation of wake words. 2021 - Sensory expands VoiceHub with speech recognition and NLU capabilities. The company initiated a new cloud platform, SensoryCloud.ai. 2022-Sensory rolls out SensoryCloud.ai with speech to text, text to speech, face & voice biometrics 2024- Sensory Automotive & TrulyNatural Speech-to-text On-Device launched == Technology and products == Sensory originally developed both hardware (Integrated Circuit - IC or "chip") and software platforms but migrated to software only around 2005 and added cloud and hybrid computing capabilities in 2021. Sensory's RSC-164 IC (Integrated Circuit or "chip") was used on NASA's Mars Polar Lander in the Mars Microphone on the Lander. Speech Synthesis SC-6x chips – acquired some speech synthesis technology from Texas Instruments. Sensory’s embedded AI solutions include the following: TrulyHandsfree (THF) - wake word detection and phrase spotting. TrulyNatural (TNL) - large vocabulary continuous speech recognition with NLU. TrulySecure (TS) - face and voice biometrics. TrulySecureSpeakerVerification (TSSV) - speaker and sound identification. VoiceHub - Online portal for creating custom wake words and speech recognition models with NLU. Sensory Automotive- Sensory Automotive is a full voice and vision suite of AI technologies that operate efficiently in the car without connecting to a network. The cloud initiative, SensoryCloud.ai, is targeting Speech To Text (STT), Text To Speech (TTS), Wake Word verification, face and voice recognition, and sound identification.

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  Payment tokenization

  Payment tokenization is a data security process that replaces sensitive payment information, such as credit card numbers, with a unique identifier or "token." This token can be used in place of actual data during transactions but has no exploitable value if breached, thereby reducing the risk of data theft and fraud. == Overview == Payment tokenization is generally categorized into two types: security tokens and payment tokens. Security tokens, also known as post-authorization tokens, are used to replace sensitive information like Primary Account Numbers (PANs), such as credit card numbers either after a payment is authorized or for storing data securely (data-at-rest), such as in merchant databases. These models have been in use since the mid-2000s, following the introduction of the Payment Card Industry Data Security Standard in 2004, which established standards for safeguarding cardholder data. The Payment Card Industry Security Standards Council's 2011 Tokenization Guidelines and the proposed American National Standards Institute X9 standards emphasize using tokens primarily to secure sensitive information, not as replacements for payment credentials processed over financial networks. Traditionally, merchants stored PANs to support backend operations such as settlements, reconciliations, chargebacks, loyalty programs, and customer service. However, with the adoption of security tokenization, merchants can substitute PANs with tokens in their systems. This not only reduces their exposure to fraud but also helps minimize the scope and cost of PCI-DSS compliance, offering a more secure and efficient way to manage cardholder data. == Applications == Payment tokenization is widely used by mobile wallets such as Apple Pay, Google Pay, and Samsung Pay use tokenization to safely store card data on devices. E-commerce platforms rely on it to securely retain customer payment details for recurring purchases. At the physical point of sale, EMV-enabled systems use tokenization to protect card information during in-store transactions. Also, subscription billing services implement tokenization to manage and safeguard payment credentials for ongoing charges.

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  Data set (IBM mainframe)

  In the context of IBM mainframe computers in the IBM System/360 line and its successors, a data set (IBM preferred) or dataset is a computer file having a record organization. Use of this term began with, e.g., DOS/360 and OS/360, and is still used by their successors, including the current VSE and z/OS. Documentation for these systems historically preferred this term rather than file. A data set is typically stored on a direct access storage device (DASD) or magnetic tape, however unit record devices, such as punch card readers, card punches, line printers and page printers can provide input/output (I/O) for a data set (file). Data sets are not unstructured streams of bytes, but rather are organized in various logical record and block structures determined by the DSORG (data set organization), RECFM (record format), and other parameters. These parameters are specified at the time of the data set allocation (creation), for example with Job Control Language DD statements. Within a running program they are stored in the Data Control Block (DCB) or Access Control Block (ACB), which are data structures used to access data sets using access methods. Records in a data set may be fixed, variable, or “undefined” length. == Data set organization == For OS/360, the DCB's DSORG parameter specifies how the data set is organized. It may be CQ Queued Telecommunications Access Method (QTAM) in Message Control Program (MCP) CX Communications line group DA Basic Direct Access Method (BDAM) GS Graphics device for Graphics Access Method(GAM) IS Indexed Sequential Access Method (ISAM) MQ QTAM message queue in application PO Partitioned Organization PS Physical Sequential among others. Data sets on tape may only be DSORG=PS. The choice of organization depends on how the data is to be accessed, and in particular, how it is to be updated. Programmers utilize various access methods (such as QSAM or VSAM) in programs for reading and writing data sets. Access method depends on the given data set organization. == Record format (RECFM) == Regardless of organization, the physical structure of each record is essentially the same, and is uniform throughout the data set. This is specified in the DCB RECFM parameter. RECFM=F means that the records are of fixed length, specified via the LRECL parameter. RECFM=V specifies a variable-length record. V records when stored on media are prefixed by a Record Descriptor Word (RDW) containing the integer length of the record in bytes and flag bits. With RECFM=FB and RECFM=VB, multiple logical records are grouped together into a single physical block on tape or DASD. FB and VB are fixed-blocked, and variable-blocked, respectively. RECFM=U (undefined) is also variable length, but the length of the record is determined by the length of the block rather than by a control field. The BLKSIZE parameter specifies the maximum length of the block. RECFM=FBS could be also specified, meaning fixed-blocked standard, meaning all the blocks except the last one were required to be in full BLKSIZE length. RECFM=VBS, or variable-blocked spanned, means a logical record could be spanned across two or more blocks, with flags in the RDW indicating whether a record segment is continued into the next block and/or was continued from the previous one. This mechanism eliminates the need for using any "delimiter" byte value to separate records. Thus data can be of any type, including binary integers, floating-point, or characters, without introducing a false end-of-record condition. The data set is an abstraction of a collection of records, in contrast to files as unstructured streams of bytes. == Partitioned data set == A partitioned data set (PDS) is a data set containing multiple members, each of which holds a separate sub-data set, similar to a directory in other types of file systems. This type of data set is often used to hold load modules (old format bound executable programs), source program libraries (especially Assembler macro definitions), ISPF screen definitions, and Job Control Language. A PDS may be compared to a Zip file or COM Structured Storage. A Partitioned Data Set can only be allocated on a single volume and have a maximum size of 65,535 tracks. Besides members, a PDS contains also a directory. Each member can be accessed indirectly via the directory structure. Once a member is located, the data stored in that member are handled in the same manner as a PS (sequential) data set. Whenever a member is deleted, the space it occupied is unusable for storing other data. Likewise, if a member is re-written, it is stored in a new spot at the back of the PDS and leaves wasted “dead” space in the middle. The only way to recover “dead” space is to perform file compression. Compression, which is done using the IEBCOPY utility, moves all members to the front of the data space and leaves free usable space at the back. (Note that in modern parlance, this kind of operation might be called defragmentation or garbage collection; data compression nowadays refers to a different, more complicated concept.) PDS files can only reside on DASD, not on magnetic tape, in order to use the directory structure to access individual members. Partitioned data sets are most often used for storing multiple job control language files, utility control statements, and executable modules. An improvement of this scheme is a Partitioned Data Set Extended (PDSE or PDS/E, sometimes just libraries) introduced with DFSMSdfp for MVS/XA and MVS/ESA systems. A PDS/E library can store program objects or other types of members, but not both. BPAM cannot process a PDS/E containing program objects. PDS/E structure is similar to PDS and is used to store the same types of data. However, PDS/E files have a better directory structure which does not require pre-allocation of directory blocks when the PDS/E is defined (and therefore does not run out of directory blocks if not enough were specified). Also, PDS/E automatically stores members in such a way that compression operation is not needed to reclaim "dead" space. PDS/E files can only reside on DASD in order to use the directory structure to access individual members. == Generation Data Group == A Generation Data Group (GDG) is a group of non-VSAM data sets that are successive generations of historically-related data stored on an IBM mainframe (running OS/360 and its successors or DOS/360 and its successors). A GDG is usually cataloged. An individual member of the GDG collection is called a "Generation Data Set." The latter may be identified by an absolute number, ACCTG.OURGDG(1234), or a relative number: (-1) for the previous generation, (0) for the current one, and (+1) the next generation. A GDG specifies how many generations of a data set are to be kept and at what age a generation will be deleted. Whenever a new generation is created, the system checks whether one or more obsolete generations are to be deleted. The purpose of GDGs is to automate archival, using the command language JCL, the data set name given is generic. When DSN appears, the GDG data set appears along with the history number, where (0) is the most recent version (-1), (-2), ... are previous generations (+1) a new generation (see DD) Another use of GDGs is to be able to address all generations simultaneously within a JCL script without having to know the number of currently available generations. To do this, you have to omit the parentheses and the generation number in the JCL when specifying the dataset. === GDG JCL & features === Generation Data Groups are defined using either the BLDG statement of the IEHPROGM utility or the DEFINE GENERATIONGROUP statement of the newer IDCAMS utility, which allows setting various parameters. LIMIT(10) would limit the number of generations limit to 10. SCRATCH FOR (91) would retain each member, up to the limited#generations, at least 91 days. IDCAMS can also delete (and optionally uncatalog) a GDG. ==== Example ==== Creation of a standard GDG for five safety scopes, each at least 35 days old: Delete a standard GDG:

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  Data exchange

  Data exchange is the process of moving data from one information system to another. It often involves transforming data that is native to the source system into a form that is consumable by the target system or to a standardized form that is consumable by any compatible system. In particular, data exchange allows data to be shared between computer programs. Data exchange is similar to data integration except that data may be restructured with possible loss of content. There may be no way to transform a particular collection based on exchange constraints. Conversely, there may be multiple ways to transform the data, in which case one option must be identified in order to achieve compatibility between source and target. There are two main types of data exchange: broadcast and peer-to-peer (a.k.a. unicast). For broadcast, data is transmitted simultaneously to all consumers. Just as a conference call, all participants get the same information from the speaker at the same time. For peer-to-peer, data is sent to a single receiver, defined by a specific address. For example, a letter goes to just one mail box. == Single-domain == In some domains, a multiple source and target schema (proprietary data formats) may exist. An exchange or interchange format is often developed for a single domain, and then necessary routines (mappings) are written to (indirectly) transform/translate each and every source schema to each and every target schema by using the interchange format as an intermediate step. That requires less work than writing and debugging the many routines that would be required to directly translate each source schema directly to each target schema. Examples of these transformative interchange formats include: Standard Interchange Format for geospatial data; Data Interchange Format for spreadsheet data; Open Document Format for spreadsheets, charts, presentations and word processing documents; GPS eXchange Format or Keyhole Markup Language for describing GPS data; GDSII for integrated circuit layout. == Representation == A data exchange (a.k.a. interchange) language defines a domain-independent way to represent data. These languages have evolved from being markup and display-oriented to support the encoding of metadata that describes the structural attributes of the information. Practice has shown that certain types of formal languages are better suited for this task than others, since their specification is driven by a formal process instead of particular software implementation. For example, XML is a markup language that was designed to enable the creation of dialects (the definition of domain-specific sublanguages). However, it does not contain domain-specific dictionaries or fact types. Beneficial to a reliable data exchange is the availability of standard dictionaries-taxonomies and tools libraries such as parsers, schema validators, and transformation tools. === XML === The popularity of XML for data exchange on the World Wide Web has several reasons. First of all, it is closely related to the preexisting standards Standard Generalized Markup Language (SGML) and Hypertext Markup Language (HTML), and as such a parser written to support these two languages can be easily extended to support XML as well. For example, XHTML has been defined as a format that is formal XML, but understood correctly by most (if not all) HTML parsers. === YAML === YAML was designed to be human-readable and authored via a text editor with notion similar to reStructuredText and wiki syntax. YAML 1.2 also includes a shorthand notion that is compatible with JSON, and as such any JSON document is also valid YAML; this however does not hold the other way. === REBOL === REBOL was designed to be human-readable and authored via a text editor. It uses a simple free-form syntax with minimal punctuation and a rich set of data types (such as URL, email, date and time, tuple, string, tag) that respect common standards. It is designed to not need any additional meta-language, being designed in a metacircular fashion which is why the parse dialect used for definitions and transformations of REBOL dialects is also itself a dialect of REBOL. REBOL was used as a source of inspiration for JSON. === Gellish === Gellish English is a formalized subset of natural English (language), which includes a simple grammar and a large, extensible dictionary (taxonomy) that defines the general and domain specific terminology, whereas the concepts are arranged in a hierarchy, which supports inheritance of knowledge and requirements. The dictionary also includes standardized fact types. The terms and relation types together can be used to create and interpret expressions of facts, knowledge, requirements and other information. Gellish can be used in combination with SQL, RDF/XML, OWL and various other meta-languages. The Gellish standard is a combination of ISO 10303-221 (AP221) and ISO 15926. === List === The following describes and compares popular data exchange languages. Columns Schemas – Whether supports representing domain specific data structure definition Flexible – Whether supports extension of the semantic expression capabilities without modifying the schema Semantic verification – Whether supports semantic verification of the correctness of expressions in the language Dictionary – Whether includes a dictionary and a taxonomy (hierarchy) of concepts with inheritance Information model – Whether supports an information model Synonyms and homonyms – Whether supports the use of synonyms and homonyms in expressions Dialecting – Whether is available in multiple natural languages or dialects Web standard – Whether is standardized by a recognized body Transformations – Whether includes a translation to other standards Lightweight – Whether a lightweight version is available Human readable – Whether expressions are understandable without training Compatibility – Which other tools can be used or are required

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  Cups (app)

  Cups (stylized as CUPS) was a mobile app launched in New York City in April 2014. It was a mobile payment and discovery platform for independent coffee shops nearby. The app was active in more than 400 cafes in New York, San Francisco, Philadelphia, Nashville, Minneapolis and Saint Paul, and other U.S. cities. == History == Cups was founded in Israel in 2012 by Gilad Rotem and four other co-founders, who were all high school friends. The company ran a limited beta pilot in Tel Aviv and Jerusalem, featuring 80 locations, from September 2012 until September 2014. Customers received all-you-can-drink coffee at certain coffee shops in Tel Aviv for approximately $45 a month. In October 2013, the founders relocated to New York. Cups participated in the Entrepreneur's Roundtable Accelerator program and went live in New York in 2014, initially working with 50 small coffee shops in Manhattan and Brooklyn. In early 2016, the company launched 30 locations in Philadelphia in February, followed by 40 more locations in San Francisco in March. == Functionality == The Cups app gave the user a list of the nearest participating coffee shops to their current location. The app user can order a drink using the app and pay the cashier with their phone. The cashier would enter a code that entered the purchase into the app's system. The app also allowed for onboard tipping and food purchases. The company reimbursed the coffee shop and kept a portion of their sales. In early 2016, the Cups Café Network was launched, using bulk purchasing power to land discounts with service providers which would normally be reserved for larger chains. In this way, the company aimed to help its café partners compete with the larger coffee chains.

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  Outfit of the day

  Outfit of the day (commonly abbreviated OOTD) is a phrase used online by users sharing what outfits (or "fits") they wear on a particular day or occasion. The video or post often mentions where each article of clothing, shoes, jewelry, and other accessories is from. OOTD posts are typically found on social media websites, such as Tumblr, Instagram, and Pinterest, and OOTD videos on YouTube and TikTok. Motives for sharing OOTD content vary, from encouraging viewers to buy a certain product, showing off personal style, or giving outfit inspiration. == History == "Outfit of the Day" videos started as early as 2010 but gained popularity in 2019. By 2016, the hashtag "OOTD" on Instagram had over 80 million posts. OOTD videos have become popular with the average internet user, as they express one's fashion sense and style to their followers. == Use in marketing == Brands will use famous influencers to promote their products using the "outfit of the day" tactic in hopes that users will buy the product to emulate the influencer. This tactic has increased sales for many brands. Creators of OOTD content can also profit, often through brand deals or affiliate links. Vogue has a recurring segment on YouTube that shows "Every outfit (fill in celebrity name here) wears in a week." == Variants == A variant is "outfit(s) of the week" (OOTW), where a user will share multiple outfits to be worn over the course of several days or a week. OOTDs are often seen in "Get ready with me" (GRWM) videos, where a user films their morning routine. In these videos, the filmers talk about their plans for the day, what makeup products they are using to get ready, and the "Outfit of the day" they are wearing. == Criticism == Some fashion writers have suggested that the proliferation of OOTD content encourages people to buy new clothing rather than to wear already owned items. Some stylists have also proposed that OOTD content encourages users to follow trends rather than explore and find their own style.

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  Social media use in the financial services sector

  Social media in the financial services sector refers to the use of social media by the financial services sector to promote and distribute financial services. Social media is used in various aspects of the financial industry including customer service, marketing, and product development. It has enabled financial institutions to extend their reach through direct and real-time communication with customers, fostering more personal connections. It also allows individuals to talk to other individuals creating lending and trading via social groups as well as developing new financial services by fintech startup companies. In terms of marketing, social media is utilized by both traditional financial companies as well as disruptive fintech companies such as peer-to-peer lending (P2P) companies. The financial industry has used information technology since its inception in the 1960s and social media fits in with this ongoing development. Larger, traditional financial firms have integrated social media into their marketing strategies. Companies in the financial sector are subject to strict regulations that include how they use social media. In the United States, the Financial Industry Regulatory Authority (FINRA) is a key regulator that sets rules how financial firms can interact with consumers. This includes ensuring that social media posts follow financial advertising rules, such as being fair and balanced and not providing misleading information, and that financial advice is not provided by unqualified personnel, such as influencers. == History == In 2003, at the beginning of social media development, MySpace was founded as a "social networking service." It allowed people to create a profile, connect with other people, and post videos, pictures, and songs. As MySpace grew in popularity, it attracted interest from companies wishing to promote their brands on the social platform. They were joined by Facebook and in 2010 by Instagram. Financial service firms were initially slow to adapt to promotion via social media but soon joined other big firms after they saw the success other industries had in engaging with younger people. == Uses == === Branding === While companies are able to connect with more people remotely through providing online financial services, their branding strategy has shifted from customized to standardized. Prior to the outbreak of technology, most banks used customized branding where they targeted only customers in their regions. Businesses can now use technology to operate beyond their geographic location and maintain a consistent image across multiple countries with standardized branding. By being able to extend a consistent brand reputation across a wider geographic location, financial services companies can take advantage of economies of scale in advertising cost, lower administrative complexity, lower entry into new markets, and improved cross-border learning within the company. === Customer engagement === Online banking reduced face-to-face interaction between customers and their banks. Most banking transactions can now be conducted online or through mobile devices, rather than at a local branch with a teller. Social media provides a channel for firms to maintain personal contact with customers, replicating some of the interaction that was previously available at local branches. For example, a bank's Facebook page may feature an employee profile describing their job duties, which serves to present a more human face for larger institutions. === Lending === Social media is a core marketing channel for online peer-to-peer lending as well as small business lenders. Since these companies operate exclusively online, it makes sense for them to market online through social media channels. They are able to grow and find new lenders and buyers by utilizing social networks. === Trading === Social trading is an alternative way of analyzing financial data by looking at what other traders are doing and comparing, copying and discussing their techniques and strategies. Prior to the advent of social trading, investors and traders were relying on fundamental or technical analysis to form their investment decisions. Using social trading investors and traders could integrate into their investment decision-process social indicators from trading data-feeds of other traders. Investors also use platform like Reddit, Signal messaging or WeChat to create social communities to discuss investments and finance. In some cases they use this to join together using meme stocks to move financial markets, such as the 2021 GameStop short squeeze incident. They can also use social groups to launch and promote new products such as cryptocurrencies. Investing application like WeBull incorporate a forum style messaging system on each stock that is available for trading. Financial brokers such as Fidelity Investments, Interactive Brokers, and E-Trade have moved to incorporate community features in their investment apps. == Regulations == The use of social media by investors and financial services professionals for business purposes is subject to regulatory oversight, in the United States this is done primarily by the Financial Industry Regulatory Authority (FINRA). FINRA's rules, designed to protect investors from misleading information in all communications and this also applies to social media communications. This includes ensuring that social media posts follow financial advertising rules, such as being fair and balanced and not providing misleading information, and that advice is not provided by unqualified personnel, such as influencers and bank staff acting in a personal capacity. Financial firms have to maintain books and records of all interaction with customers and this includes social media. == New products and services == Social media has created entirely new products for the financial services sector, revolutionizing products and developing new industries through the merging of social technology and financial services. Fintech startups use social media to promote products to get them established. Several developing nations have used social media to leapfrog traditional financial technology; for example, WeChat Pay, which developed from the Chinese WeChat social media platform, became a major payment system in China within a few years. In 2015, according to consulting firm Accenture, 390 million people in China had registered to use mobile banking. This figure is more than the population of the United States. In the United States, the fintech company Venmo combines technology and financial services on a social platform. Other financial technology companies that have used social media to develop or promote financial products include: Lending Club – One of the first peer-to-peer lending businesses OnDeck Capital – A US online-only lending business Funding Circle – A UK-based online lending company Wise – A global online money transfers company Kabbage – A US online unsecured loan company later acquired by American Express Avant – A US online unsecured loan company Zopa – A UK online neobank providing peer-to-peer lending == Risks == === Reputational damage === Due to the real-time nature of social media, financial services companies can be impacted by potential reputational issues. Any negative experience by customers can easily be shared online and could become a viral phenomenon, those comments could likely have a detrimental effect on the company’s stock price and reputation. On the other hand, any positive experience a customer has can also be shared online. However, positive experiences are much less likely to become viral. === Scams === The nature of social media makes it easy to target individuals without being seen by the wider community, this allows scammers to target individuals. Example include romance scams such as the pig butchering scam where an individual is tricked to transfer funds or assets to the scammer over social media making it hard for law enforcement to track them or recover funds. === Customer privacy === Customer privacy is important for the financial services industry. It is critical that customer information such as a bank account numbers and other personal information is kept private. However, this information can be leaked if for example, a customer is unhappy with a bank’s service, they may tweet at the bank expressing their frustrations and include their name and account number.

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  Social media optimization

  Social media optimization (SMO) is the use of online platforms to generate income or publicity to increase the awareness of a brand, event, product or service. Types of social media involved include RSS feeds, blogging sites, social bookmarking sites, social news websites, video sharing websites such as YouTube and social networking sites such as Facebook, Instagram, TikTok and X (Twitter). SMO is similar to search engine optimization (SEO) in that the goal is to drive web traffic, and draw attention to a company or creator. SMO's focal point is on gaining organic links to social media content. In contrast, SEO's core is about reaching the top of the search engine hierarchy. In general, social media optimization refers to optimizing a website and its content to encourage more users to use and share links to the website across social media and networking sites. SMO is used to strategically create online content ranging from well-written text to eye-catching digital photos or video clips that encourages and entices people to engage with a website. Users share this content, via its weblink, with social media contacts and friends. Common examples of social media engagement are "liking and commenting on posts, retweeting, embedding, sharing, and promoting content". Social media optimization is also an effective way of implementing online reputation management (ORM), meaning that if someone posts bad reviews of a business, an SMO strategy can ensure that the negative feedback is not the first link to come up in a list of search engine results. In the 2010s, with social media sites overtaking TV as a source for news for young people, news organizations have become increasingly reliant on social media platforms for generating web traffic. Publishers such as The Economist employ large social media teams to optimize their online posts and maximize traffic, while other major publishers now use advanced artificial intelligence (AI) technology to generate higher volumes of web traffic. == Relationship with search engine optimization == Social media optimization is an increasingly important factor in search engine optimization, which is the process of designing a website in a way so that it has as high a ranking as possible on search engines. Search engines are increasingly utilizing the recommendations of users of social networks such as Reddit, Facebook, Tumblr, Twitter, YouTube, LinkedIn, Pinterest and Instagram to rank pages in the search engine result pages. The implication is that when a webpage is shared or "liked" by a user on a social network, it counts as a "vote" for that webpage's quality. Thus, search engines can use such votes accordingly to properly ranked websites in search engine results pages. Furthermore, since it is more difficult to tip the scales or influence the search engines in this way, search engines are putting more stock into social search. This, coupled with increasingly personalized search based on interests and location, has significantly increased the importance of a social media presence in search engine optimization. Due to personalized search results, location-based social media presences on websites such as Yelp, Google Places, Foursquare, and Yahoo! Local have become increasingly important. While social media optimization is related to search engine marketing, it differs in several ways. Primarily, SMO focuses on driving web traffic from sources other than search engines, though improved search engine ranking is also a benefit of successful social media optimization. Further, SMO is helpful to target particular geographic regions in order to target and reach potential customers. This helps in lead generation (finding new customers) and contributes to high conversion rates (i.e., converting previously uninterested individuals into people who are interested in a brand or organization). == Relationship with viral marketing == Social media optimization is in many ways connected to the technique of viral marketing or "viral seeding" where word of mouth is created through the use of networking in social bookmarking, video and photo sharing websites. An effective SMO campaign can harness the power of viral marketing; for example, 80% of activity on Pinterest is generated through "repinning." Furthermore, by following social trends and utilizing alternative social networks, websites can retain existing followers while also attracting new ones. This allows businesses to build an online following and presence, all linking back to the company's website for increased traffic. For example, with an effective social bookmarking campaign, not only can website traffic be increased, but a site's rankings can also be increased. In a similar way, the engagement with blogs creates a similar result by sharing content through the use of RSS in the blogosphere. Social media optimization is considered an integral part of an online reputation management (ORM) or search engine reputation management (SERM) strategy for organizations or individuals who care about their online presence. SMO is one of six key influencers that affect Social Commerce Construct (SCC). Online activities such as consumers' evaluations and advices on products and services constitute part of what creates a Social Commerce Construct (SCC). Social media optimization is not limited to marketing and brand building. Increasingly, smart businesses are integrating social media participation as part of their knowledge management strategy (i.e., product/service development, recruiting, employee engagement and turnover, brand building, customer satisfaction and relations, business development and more). Additionally, social media optimization can be implemented to foster a community of the associated site, allowing for a healthy business-to-consumer (B2C) relationship. == Origins and implementation == According to technologist Danny Sullivan, the term "social media optimization" was first used and described by marketer Rohit Bhargava on his marketing blog in August 2006. In the same post, Bhargava established the five important rules of social media optimization. Bhargava believed that by following his rules, anyone could influence the levels of traffic and engagement on their site, increase popularity, and ensure that it ranks highly in search engine results. An additional 11 SMO rules have since been added to the list by other marketing contributors. The 16 rules of SMO, according to one source, are as follows: Increase your linkability Make tagging and bookmarking easy Reward inbound links Help your content to "travel" via sharing Encourage the mashup, where users are allowed to remix content Be a user resource, even if it doesn't help you (e.g., provide resources and information for users) Reward helpful and valuable users Participate (join the online conversation) Know how to target your audience Create new, quality content ("web scraping" of existing online content is ignored by good search engines) Be "real" in the tone and style of the posts Don't forget your roots; be humble Don't be afraid to experiment, innovate, try new things and "stay fresh" Develop an SMO strategy Choose your SMO tactics wisely Make SMO a key part of your marketing process and develop company best practices Bhargava's initial five rules were more specifically designed to SMO, while the list is now much broader and addresses everything that can be done across different social media platforms. According to author and CEO of TopRank Online Marketing, Lee Odden, a Social Media Strategy is also necessary to ensure optimization. This is a similar concept to Bhargava's list of rules for SMO. The Social Media Strategy may consider: Objectives e.g. creating brand awareness and using social media for external communications. Listening e.g. monitoring conversations relating to customers and business objectives. Audience e.g. finding out who the customers are, what they do, who they are influenced by, and what they frequently talk about. It is important to work out what customers want in exchange for their online engagement and attention. Participation and content e.g. establishing a presence and community online and engaging with users by sharing useful and interesting information. Measurement e.g. keeping a record of likes and comments on posts, and the number of sales to monitor growth and determine which tactics are most useful in optimizing social media. According to Lon Safko and David K. Brake in The Social Media Bible, it is also important to act like a publisher by maintaining an effective organizational strategy, to have an original concept and unique "edge" that differentiates one's approach from competitors, and to experiment with new ideas if things do not work the first time. If a business is blog-based, an effective method of SMO is using widgets that allow users to share content to their personal social media platforms. This will ultimately reach a wider target audience and drive mor

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  ASR-complete

  ASR-complete is, by analogy to "NP-completeness" in complexity theory, a term to indicate that the difficulty of a computational problem is equivalent to solving the central automatic speech recognition problem, i.e. recognize and understanding spoken language. Unlike "NP-completeness", this term is typically used informally. Such problems are hypothesised to include: Spoken natural language understanding Understanding speech from far-field microphones, i.e. handling the reverbation and background noise These problems are easy for humans to do (in fact, they are described directly in terms of imitating humans). Some systems can solve very simple restricted versions of these problems, but none can solve them in their full generality.

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  IWARP

  iWARP is a computer networking protocol that implements remote direct memory access (RDMA) for efficient data transfer over Internet Protocol networks. Contrary to some accounts, iWARP is not an acronym. Because iWARP is layered on Internet Engineering Task Force (IETF)-standard congestion-aware protocols such as Transmission Control Protocol (TCP) and Stream Control Transmission Protocol (SCTP), it makes few requirements on the network, and can be successfully deployed in a broad range of environments. == History == In 2007, the IETF published five Request for Comments (RFCs) that define iWARP: RFC 5040 A Remote Direct Memory Access Protocol Specification is layered over Direct Data Placement Protocol (DDP). It defines how RDMA Send, Read, and Write operations are encoded using DDP into headers on the network. RFC 5041 Direct Data Placement over Reliable Transports is layered over MPA/TCP or SCTP. It defines how received data can be directly placed into an upper layer protocols receive buffer without intermediate buffers. RFC 5042 Direct Data Placement Protocol (DDP) / Remote Direct Memory Access Protocol (RDMAP) Security analyzes security issues related to iWARP DDP and RDMAP protocol layers. RFC 5043 Stream Control Transmission Protocol (SCTP) Direct Data Placement (DDP) Adaptation defines an adaptation layer that enables DDP over SCTP. RFC 5044 Marker PDU Aligned Framing for TCP Specification defines an adaptation layer that enables preservation of DDP-level protocol record boundaries layered over the TCP reliable connected byte stream. These RFCs are based on the RDMA Consortium's specifications for RDMA over TCP. The RDMA Consortium's specifications are influenced by earlier RDMA standards, including Virtual Interface Architecture (VIA) and InfiniBand (IB). Since 2007, the IETF has published three additional RFCs that maintain and extend iWARP: RFC 6580 IANA Registries for the Remote Direct Data Placement (RDDP) Protocols published in 2012 defines IANA registries for Remote Direct Data Placement (RDDP) error codes, operation codes, and function codes. RFC 6581 Enhanced Remote Direct Memory Access (RDMA) Connection Establishment published in 2011 fixes shortcomings with iWARP connection setup. RFC 7306 Remote Direct Memory Access (RDMA) Protocol Extensions published in 2014 extends RFC 5040 with atomic operations and RDMA Write with Immediate Data. == Protocol == The main component in the iWARP protocol is the Direct Data Placement Protocol (DDP), which permits the actual zero-copy transmission. DDP itself does not perform the transmission; the underlying protocol (TCP or SCTP) does. However, TCP does not respect message boundaries; it sends data as a sequence of bytes without regard to protocol data units (PDU). In this regard, DDP itself may be better suited for SCTP, and indeed the IETF proposed a standard RDMA over SCTP. To run DDP over TCP requires a tweak known as marker PDU aligned (MPA) framing to guarantee boundaries of messages. Furthermore, DDP is not intended to be accessed directly. Instead, a separate RDMA protocol (RDMAP) provides the services to read and write data. Therefore, the entire RDMA over TCP specification is really RDMAP over DDP over either MPA/TCP or SCTP. All of these protocols can be implemented in hardware. Unlike IB, iWARP only has reliable connected communication, as this is the only service that TCP and SCTP provide. The iWARP specification omits other features of IB, such as Send with Immediate Data operations. With RFC 7306, the IETF is working to reduce these omissions. == Implementation == Because a kernel implementation of the TCP stack can be seen as a bottleneck, the protocol is typically implemented in hardware RDMA network interface controllers (rNICs). As simple data losses are rare in tightly coupled network environments, the error-correction mechanisms of TCP may be performed by software while the more frequently performed communications are handled strictly by logic embedded on the rNIC. Similarly, connections are often established entirely by software and then handed off to the hardware. Furthermore, the handling of iWARP specific protocol details is typically isolated from the TCP implementation, allowing rNICs to be used for both as RDMA offload and TCP offload (in support of traditional sockets based TCP/IP applications). The portion of the hardware implementation used for implementing the TCP protocol is known as the TCP Offload Engine (TOE). TOE itself does not prevent copying on the reception side, and must be combined with RDMA hardware for zero-copy results. The RDMA / TCP specification is a set of different wire protocols intended to be implemented in hardware (though it seems feasible to emulate it in software for compatibility but without the performance benefits). == Interfaces == iWARP is a protocol, not an implementation, but defines protocol behavior in terms of the operations that are legal for the protocol, known as Verbs. As such, iWARP does not have any single standard programming interface. However, programming interfaces tend to very closely correspond to the Verbs. Several programmatic interfaces have been proposed, including OpenFabrics Verbs, Network Direct, uDAPL, kDAPL, IT-API, and RNICPI. Implementations of some of these interfaces are available for different platforms, including Windows and Linux. == Services available == Networking services implemented over iWARP include those offered in the OpenFabrics Enterprise Distribution (OFED) by the OpenFabrics Alliance for Linux operating systems, and by Microsoft Windows via Network Direct. NVMe over Fabrics (NVMEoF) iSCSI Extensions for RDMA (iSER) Server Message Block Direct (SMB Direct) Sockets Direct Protocol (SDP) SCSI RDMA Protocol (SRP) Network File System over RDMA (NFS over RDMA) GPUDirect

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  Social employee

  A social employee is a worker operating within a social business model. Following an organization's social computing guidelines, social employees use social media tools both for internal workflow and collaboration purposes and for external engagement with customers, prospects and stakeholders through a combination of social media marketing, content marketing, social marketing, and social selling. Social employee programs are considered to be as much about culture and engagement as they are about business processes and best practices. In addition to increased leads and sales, social employee best practices are said to improve business outcomes important to social media marketing, such as increased connections and web traffic, improved brand identification and "chatter", and better customer advocacy. == Overview == The term "social employee" was first introduced to describe those exhibiting the emerging characteristics of workers operating under a social business model. The term is often used interchangeably with similar designations like "employee advocate" or "social employee advocate". Crucial to the perceived value of the social employee is the concept of the digital footprint. While organizations are able to generate large bases of followers through social media, research shows that brand marketing and engagement efforts through these networks are not as effective as those of individual employees. In fact, some research indicates that employee experts are more trusted than any other member of an organization. Because of this, social employee programs are designed to train, empower, and support employee engagement efforts in the hopes of authentically engaging larger communities, increasing the frequency of shares, reviews, and other forms of "earned media" and expanding the brand's presence on the web. == The personal or employee brand == A foundational concept of the social employee is the idea of the personal or employee brand. This concept first gained popular attention in a 1997 FastCompany article by business leader Tom Peters titled "The Brand Called You". In the article, Peters argued that the premium placed on branding impacted workers' lives to such an extent that creating and cultivating a distinct personal brand had become a professional necessity. According to Peters, doing so built trust, loyalty, visibility, influence, and employability. With increased adoption of social media tools by both businesses and consumers in the early 21st century, many business leaders became increasingly concerned with social engagement, both internally among employees and externally with customers and other stakeholders. While many in the business community acknowledged the potential social tools had for improved collaboration, productivity, and brand messaging, the concern that employees would misrepresent their brand, disclose proprietary information, or otherwise damage their company's reputation or ability to conduct business persisted. As a result, many began to advocate for employee branding as a solution to this problem. This helped give new meaning to the concept of brand ambassadorship, positioning everyday employees in public, and potentially high-profile, engagement roles. == Characteristics == === Engaged === Because social employee advocacy is dependent on the perceived authenticity of the employee, engagement is highly valued in social organizations. Further, data show the measurable impact of employee engagement on organizational productivity and profitability: Happy employees were found to be 12 percent more productive. In one study, engaged employees were found to be 38 percent more likely to produce at above-average rates. In another, organizations with engaged employees had a 19 percent higher than average shareholder return, while organizations with disengaged employees experienced shareholder return that was 44 percent below average. Engaged companies were found to outperform disengaged companies by up to 202 percent. Companies with strong focus on culture were found to have an average 13.9 percent turnover rate, while those with a low focus experience were found to have a 48.4 percent turnover rate. === Flexible job environment and work–life balance === The number of professionals working mobile or remote has risen considerably since 2010. While estimates vary, one study found that number of organizations with mobile or remote employees is expected to rise from 24 percent in 2012 to 89 percent by 2020. Other research has estimated that by 2020, 105.4 million professionals will work remotely in America, about 72.3 percent of the total workforce. This change has been linked to a rise in social technologies, including biometrics, wearables, near-field communications, and augmented reality. Social employees have also put a greater emphasis on work–life balance, with many believing that advances in technology can directly support efforts in this area. Purported benefits of this shift include a more flexible workforce, reduced business costs, and greater organizational leverage in attracting and retaining top talent. === Buys into the brand's story === In 2009, thought leader Simon Sinek presented a speech called "How Great Leaders Inspire Action" at a TEDxPugetSound event. Sinek's central argument in this speech was, "People don't buy what you do. They buy why you do it." This concept—that the story behind a business or product offering is a more compelling sales tool than the product itself—is frequently cited in social media marketing as a way to build authentic connections with stakeholders. However, others have argued that for employees to share a brand's story authentically, they must be engaged in that story themselves, and as a result, many companies have made storytelling part of their culture programs. === Collaborative === An implicit tenet in social business is that social technologies aren't a barrier to productivity, but rather a path to increased connectivity. The shift in enterprise software systems like IBM Connections to incorporate social communication models, such as mentions, wikis, and newsfeeds, reflects the changing communication dynamics within business. With an increase in diversity and sophistication in collaborative software platforms, social organizations have sought to find new creative ways to utilize these tools and secure employee buy-in around them. Crowdsourcing has also become popular in social businesses. Examples include AT&T's program The Innovation Pipeline (TIP), begun in 2009, which has generated over 28,000 ideas that have led to over 75 projects with funding exceeding $44 million. IBM has also put considerable resources into such processes, producing its social computing guidelines through employee crowdsourcing, as well as its Connections platform through the Technology Adoption Program (TAP), a more formalized crowdsourcing initiative. Another popular form of internal collaboration is the hack day, or hackathon. Organizations such as Netflix, Facebook, and IBM use hack days to pull employees out of their day-to-day work environments and encourage them to collaborate in nontraditional ways in an attempt to drive disruptive innovation. Social employees are often encouraged to seek external collaboration opportunities with customers and prospects. For example, Procter & Gamble introduced the Live Well Collaborative to connect with external stakeholders and develop products and services for the 50+ demographic. === Social listener === A social listener is someone who engages in social listening, or social media monitoring, for professional means. Social employees can use social media monitoring for a variety of reasons, including professional development, industry news and trends, and gauging market sentiment. Some have argued that social listening is one of the most important components of social business, as it enables organizations to collect rich market data, make more informed strategic decisions, and respond to customer needs more authentically. === Customer-centric === Advocates of customer-centricity in social business argue that social media has changed the dynamic from one-way brand messaging to shared interactions between brand and customer. Brand and customer engagement is seen as a means of creating more lasting connections with customers and prospects and empowering them to become brand promoters. Customer-centric interactions are seen to have distinct value to brands, as research shows that prospects are far more likely to trust brand-related messaging from a friend or family member than they are from a brand. As a means of building social employees, some social advocates have also called for a broader definition of customer to include the employees themselves. In the book The Pursuit of Social Business Excellence, authors Vala Afshar and Brad Martin made the following argument: A social business operates with the guiding principle that each employee's responsi

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