AI Data Visualization Tools

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

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  Luminoso

  Luminoso is a Cambridge, MA-based text analytics and artificial intelligence company. It spun out of the MIT Media Lab and its crowd-sourced Open Mind Common Sense (OMCS) project. The company has raised $20.6 million in financing, and its clients include Sony, Autodesk, Scotts Miracle-Gro, and GlaxoSmithKline. == History == Luminoso was co-founded in 2010 by Dennis Clark, Jason Alonso, Robyn Speer, and Catherine Havasi, a research scientist at MIT in artificial intelligence and computational linguistics. The company builds on the knowledge base of MIT’s Open Mind Common Sense (OMCS) project, co-founded in 1999 by Havasi, who continues to serve as its director. The OCMS knowledge base has since been combined with knowledge from other crowdsourced resources to become ConceptNet. ConceptNet consists of approximately 28 million statements in 304 languages, with full support for 10 languages and moderate support for 77 languages. ConceptNet is a resource for making an AI that understands the meanings of the words people use. During the World Cup in June 2014, the company provided a widely reported real-time sentiment analysis of the U.S. vs. Germany match, analyzing 900,000 posts on Twitter, Facebook and Google+. == Applications == The company uses artificial intelligence, natural language processing, and machine learning to derive insights from unstructured data such as contact center interactions, chatbot and live chat transcripts, product reviews, open-ended survey responses, and email. Luminoso's software identifies and quantifies patterns and relationships in text-based data, including domain-specific or creative language. Rather than human-powered keyword searches of data, the software automates taxonomy creation around concepts, allowing related words and phrases to be dynamically generated and tracked. Commercial applications include analyzing, prioritizing, and routing contact center interactions; identifying consumer complaints before they begin to trend; and tracking sentiment during product launches. The software natively analyzes text in fourteen languages, as well as emoji. == Products == Luminoso's technology can be accessed via two products: Luminoso Daylight and Luminoso Compass. Luminoso Daylight enables a deep-dive analysis into batch or real-time data, whereas Luminoso Compass automates the categorization of real-time data. Both products offer a user interface as well as an API. Luminoso's products can be implemented through either a cloud-based or an on-premise solution. == Research == Luminoso continues to actively conduct research in natural language processing and word embeddings and regularly participates in evaluations such as SemEval. At SemEval 2017, Luminoso participated in Task 2, measuring the semantic similarity of word pairs within and across five languages. Its solution outperformed all competing systems in every language pair tested, with the exception of Persian. == Recognition == Luminoso has been listed as a "Cool Vendor in AI for Marketing" by Gartner, and has also been named a "Boston Artificial Intelligence Startup to Watch" by BostInno. In May 2017, Luminoso was recognized as having the Best Application for AI in the Enterprise by AI Business, and was also shortlisted as the Best AI Breakthrough and Best Innovation in NLP. == Competitors == Major competitors include Clarabridge and Lexalytics. == Investors == The company raised $1.5 million from angel investors led by Basis Technology in 2012. Its first institutional funding round of $6.5 was completed in July 2014, led by Acadia Woods with participation from Japan’s Digital Garage. The company followed that with a $10M series B funding round in December 2018, led by DVI Equity Partners, with participation from Liberty Global Ventures, DF Enterprises, Raptor Holdco, Acadia Woods Partners, and Accord Ventures, among others.

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  Menu hack

  A menu hack is a non-standard method of ordering food, usually at fast-food or fast casual restaurants, that offers a different result than what is explicitly stated on a menu. Menu hacks may range from a simple alternate flavor to "gaming the system" in order to obtain more food than normal. They are often spread on social media platforms such as TikTok, and are more popular with Generation Z, which has been known to customize their orders more than previous generations. Hacks are sometimes officially added to the menu after their popularity grows. However, in some cases, they have been criticized for overburdening fast food employees with outlandish requests, sparking debate as to whether certain menu hacks are unethical. The list of all possible menu hacks is called a secret menu. == History == The term "menu hack" stems from hacker culture and its tradition of overcoming previously imposed limitations. However, the tradition of ordering from a secret menu dates back to the early days of fast food. "Animal style" fries, a word of mouth menu item ordered from In-N-Out since the 1960s, was rumored to have been created by local surfers. In the Information Age, the rise of social media gave influencers the ability to communicate unique food combinations to their followers, which proved to go viral easily. Design mistakes in food ordering apps also proved to be easily exploitable. In some cases, these hacks boosted the profile of brands on social media, while in others, they caused financial harm when the company was unprepared to handle the sudden influx of unusual orders. One restaurant chain notable for the phenomenon is Chipotle Mexican Grill. A viral hack from Alexis Frost, suggesting a quesadilla with fajita vegetables inside, dipped in Chipotle vinaigrette mixed with sour cream, obtained 1.9 million views on TikTok, overloading the chain's workers, who had to work harder to prepare more vegetables and vinaigrette. Some restaurants began to deny the dish to customers, forcing them to only order meat and cheese on quesadillas. The company ultimately left the dish on the menu, but urged customers to stop ordering it via social media. When it later officially added the Fajita Quesadilla to the menu, digital sales nearly doubled. A method to order nachos, which are not officially on the menu, was also noted by customers. Starbucks is also famous for menu hacks, including the Pink Drink, a "Barbiecore" beverage in which coconut milk replaced the water in the strawberry açaí refresher. After it went viral, the company made it a permanent menu item and distributed it bottled in grocery stores. == Controversy == Menu hacks have been subject to a growing backlash, with employees stating that they "dread" younger customers due to the proliferation of unusual orders. Service industry workers, already overworked and underpaid, have called the rise of menu hacks and their difficulty to make an additional reason to unionize and demand higher wages.

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  Data Management Association

  The Data Management Association (DAMA), formerly known as the Data Administration Management Association, is a global not-for-profit organization which aims to advance concepts and practices about information management and data management. It describes itself as vendor-independent, all-volunteer organization, and has a membership consisting of technical and business professionals. Its international branch is called DAMA International (or DAMA-I), and DAMA also has various continental and national branches around the world. == History == The Data Management Association International was founded in 1980 in Los Angeles. Other early chapters were: San Francisco, Portland, Seattle, Minneapolis, New York, and Washington D.C. == Data Management Body of Knowledge == DAMA has published the Data Management Body of Knowledge (DMBOK), which contains suggestions on best practices and suggestions of a common vernacular for enterprise data management. The first edition (DAMA-DMBOK) was published on 2009 November 1, the second edition (DAMA-DMBOK2) was published on 2017 July 1., and the Revised second edition (DAMA-DMBOK2 rev.2) was published on 2019 March 19. DMBOK has been described by the authors as being an "equivalent" to the Project Management Body of Knowledge (PMBOK) and Business Analysis Body of Knowledge (BABOK). It encompasses topics such as data architecture, security, quality, modelling, governance, big data, data science, and more. DMBOK also includes the DAMA Data Wheel, an infographic which represents core data management practices. The center of the infographic is data governance, and the surrounding segments each represent a different aspect of data management: Data architecture Data modeling and design Data storage and operations Data security Data integration and interoperability Document management Content management Master data management Reference data and master data Data warehousing Metadata management Data quality Business intelligence Data science == Professional Accreditation == DAMA also provides a professional data management certification for individuals known as a Certified Data Management Professional (CDMP), which is based on the DMBOK as a study reference. There are four levels of certification based on career experience and exam results. The highest level, Fellow, requires 25 years of experience and nomination by DAMA members. It is an example of one of many competing certifications for data management professionals.

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  Cryptovirology

  Cryptovirology refers to the study of cryptography use in malware, such as ransomware and asymmetric backdoors. Traditionally, cryptography and its applications are defensive in nature, and provide privacy, authentication, and security to users. Cryptovirology employs a twist on cryptography, showing that it can also be used offensively. It can be used to mount extortion based attacks that cause loss of access to information, loss of confidentiality, and information leakage, tasks which cryptography typically prevents. The field was born with the observation that public-key cryptography can be used to break the symmetry between what an antivirus analyst sees regarding malware and what the attacker sees. The antivirus analyst sees a public key contained in the malware, whereas the attacker sees the public key contained in the malware as well as the corresponding private key (outside the malware) since the attacker created the key pair for the attack. The public key allows the malware to perform trapdoor one-way operations on the victim's computer that only the attacker can undo. == Overview == The field encompasses covert malware attacks in which the attacker securely steals private information such as symmetric keys, private keys, PRNG state, and the victim's data. Examples of such covert attacks are asymmetric backdoors. An asymmetric backdoor is a backdoor (e.g., in a cryptosystem) that can be used only by the attacker, even after it is found. This contrasts with the traditional backdoor that is symmetric, i.e., anyone that finds it can use it. Kleptography, a subfield of cryptovirology, is the study of asymmetric backdoors in key generation algorithms, digital signature algorithms, key exchanges, pseudorandom number generators, encryption algorithms, and other cryptographic algorithms. The NIST Dual EC DRBG random bit generator has an asymmetric backdoor in it. The EC-DRBG algorithm utilizes the discrete-log kleptogram from kleptography, which by definition makes the EC-DRBG a cryptotrojan. Like ransomware, the EC-DRBG cryptotrojan contains and uses the attacker's public key to attack the host system. The cryptographer Ari Juels indicated that NSA effectively orchestrated a kleptographic attack on users of the Dual EC DRBG pseudorandom number generation algorithm and that, although security professionals and developers have been testing and implementing kleptographic attacks since 1996, "you would be hard-pressed to find one in actual use until now." Due to public outcry about this cryptovirology attack, NIST rescinded the EC-DRBG algorithm from the NIST SP 800-90 standard. Covert information leakage attacks carried out by cryptoviruses, cryptotrojans, and cryptoworms that, by definition, contain and use the public key of the attacker is a major theme in cryptovirology. In "deniable password snatching," a cryptovirus installs a cryptotrojan that asymmetrically encrypts host data and covertly broadcasts it. This makes it available to everyone, noticeable by no one (except the attacker), and only decipherable by the attacker. An attacker caught installing the cryptotrojan claims to be a virus victim. An attacker observed receiving the covert asymmetric broadcast is one of the thousands, if not millions of receivers, and exhibits no identifying information whatsoever. The cryptovirology attack achieves "end-to-end deniability." It is a covert asymmetric broadcast of the victim's data. Cryptovirology also encompasses the use of private information retrieval (PIR) to allow cryptoviruses to search for and steal host data without revealing the data searched for even when the cryptotrojan is under constant surveillance. By definition, such a cryptovirus carries within its own coding sequence the query of the attacker and the necessary PIR logic to apply the query to host systems. == History == The first cryptovirology attack and discussion of the concept was by Adam L. Young and Moti Yung, at the time called "cryptoviral extortion" and it was presented at the 1996 IEEE Security & Privacy conference. In this attack, a cryptovirus, cryptoworm, or cryptotrojan contains the public key of the attacker and hybrid encrypts the victim's files. The malware prompts the user to send the asymmetric ciphertext to the attacker who will decipher it and return the symmetric decryption key it contains for a fee. The victim needs the symmetric key to decrypt the encrypted files if there is no way to recover the original files (e.g., from backups). The 1996 IEEE paper predicted that cryptoviral extortion attackers would one day demand e-money, long before Bitcoin even existed. Many years later, the media relabeled cryptoviral extortion as ransomware. In 2016, cryptovirology attacks on healthcare providers reached epidemic levels, prompting the U.S. Department of Health and Human Services to issue a Fact Sheet on Ransomware and HIPAA. The fact sheet states that when electronic protected health information is encrypted by ransomware, a breach has occurred, and the attack therefore constitutes a disclosure that is not permitted under HIPAA, the rationale being that an adversary has taken control of the information. Sensitive data might never leave the victim organization, but the break-in may have allowed data to be sent out undetected. California enacted a law that defines the introduction of ransomware into a computer system with the intent of extortion as being against the law. == Examples == === Tremor virus === While viruses in the wild have used cryptography in the past, the only purpose of such usage of cryptography was to avoid detection by antivirus software. For example, the tremor virus used polymorphism as a defensive technique in an attempt to avoid detection by anti-virus software. Though cryptography does assist in such cases to enhance the longevity of a virus, the capabilities of cryptography are not used in the payload. The One-half virus was amongst the first viruses known to have encrypted affected files. === Tro_Ransom.A virus === An example of a virus that informs the owner of the infected machine to pay a ransom is the virus nicknamed Tro_Ransom.A. This virus asks the owner of the infected machine to send $10.99 to a given account through Western Union. Virus.Win32.Gpcode.ag is a classic cryptovirus. This virus partially uses a version of 660-bit RSA and encrypts files with many different extensions. It instructs the owner of the machine to email a given mail ID if the owner desires the decryptor. If contacted by email, the user will be asked to pay a certain amount as ransom in return for the decryptor. === CAPI === It has been demonstrated that using just 8 different calls to Microsoft's Cryptographic API (CAPI), a cryptovirus can satisfy all its encryption needs. == Other uses of cryptography-enabled malware == Apart from cryptoviral extortion, there are other potential uses of cryptoviruses, such as deniable password snatching, cryptocounters, private information retrieval, and in secure communication between different instances of a distributed cryptovirus.

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  Kubity

  Kubity is a cloud-based 3D communication tool that works on desktop computers, the web, smartphones, tablets, augmented reality gear, and virtual reality glasses. Kubity is powered by several proprietary 3D processing engines including "Paragone" and "Etna" that prepare the 3D file for transfer over mobile devices. Kubity has practical applications for architecture, interior design, engineering, product design, film, and video games among others. The majority of its users create 3D models using SketchUp or Autodesk Revit software. Kubity products include the Kubity web app and Kubity Go (a mobile application for iOS and Android). Kubity is compatible across many platforms, devices and operating systems including: iOS, Android, Firefox, Chrome, Windows, MacOS, and Linux. == History == Kubity was created by SPK Technology (ex Kubity S.A.S.), a Paris-based software company specializing in automatic 3D data optimization and visualization. Founded in 2012 by a group of software engineers and an urban projects developer, they united around a simple idea: create a way for anyone, anywhere to simply and intuitively explore 3D models on smartphones and computers. In order to bring architects, engineers and designers together with their clients around a 3D model, it was essential to develop an interactive platform that supported multiple desktop and mobile devices for instantaneous and fluid 3D navigation. With specifications in place, 15 engineers fused together several technologies: 3D design, data compression, decimation and rendering optimization, web and mobile transfer, and virtual reality headset integration. In January 2014, the first public Kubity prototype (1.0 Amethyst) was launched to a small group of beta testers with a plug-in that allowed users to import 3D models from SketchUp to their browser. A global release was announced in April 2014 at the SketchUp Basecamp in Vail, Colorado. In May 2015, Kubity launched a web application that worked using WebGL technology (2.0 Citrine). For the first time, users were able to drag and drop any SketchUp file in a web browser without having to install a plug-in. In December 2015, Kubity launched a mobile application on the App Store for iPhone, iPod, and iPad as well as on Google Play for Android devices (3.0 Druzy). In November 2016, Kubity launched support for Oculus Rift and HTC Vive (4.0 Emerald). Beginning in November 2017, Kubity launched a full suite rollout of mobile applications over six months that included Kubity AR for augmented reality, Kubity VR for virtual reality, and Kubity Mirror for remote presentations and screen mirroring (5.0 Feldspar). In September 2018, a one-click plugin for SketchUp and Revit (Kubity PRO), along with a mobile-first revamp of Kubity Go was launched, allowing PRO-to-Go device pairing for automatic mobile sync (6.0 Gypsum). In early 2019, the Kubity Go application was updated to include fully integrated AR, VR, and screen mirroring functionalities, killing off the dedicated companion apps Kubity AR, Kubity VR and Kubity Mirror in the process (7.0 Heliotrope). In January 2020, support for the Kubity PRO plugin for SketchUp and Revit was migrated to a SketchUp-only web app. == Technology == Kubity is powered by a proprietary 3D crystallization engine known as "Paragone"; a technology developed by SPK Technology. Paragone takes constrained information from a 3D file and runs it through the "BlockWave" algorithm (US Patent 10,482.629), also developed by SPK Technology. BlockWave is a multiphase optimization algorithm that combines 3D design, data compression, decimation and rendering optimization, web and mobile transfer, and mixed reality headset integration to create a crystallized universal format of the original file. One phase of the BlockWave algorithm is based on the quadric-based polygonal surface simplification algorithm, performed using predefined heuristics, and is associated with a plurality of simplified versions of the 3D model, each version being associated with a predefined level of detail adapted to the user specific end device. BlockWave extracts data content, geometry and textures, then sets quadrics for each top of the original 3D model, and identifies pairs of adjacent tops linked by vertices. The algorithm uses a local collapsing operator and a top-plan error metric to obtain a fixed number of faces or a maximum defined error; 3D meshing is simplified by replacing two points with one, then deleting the degrading faces and updating adjacent relations. Once decimation is completed, texture optimization is set using texture target parameters allowing maximized GPU memory to improve computing time. With texture encoding completed, the crystallized universal 3D file can now be easily opened on any user-specific end device and played across most digital devices with real-time rendering. == Features == === 3D Crystallization === A user converts (or crystallizes) a 3D file by exporting it with the Kubity web app. Crystallization adds features like AR/VR and cinematic fly-through tour as well as assigns the model a dedicated QR code. === Automatic Mobile Sync === When a 3D model is exported, it is automatically synced to Kubity Go on the user's mobile device. From there, it can be accessed, explored, and shared with others with or without an internet connection. === Security and Management === User models can be managed all in one place on Kubity Go or in a browser from their account. Models can be renamed, password-protected, shared, and played. === Augmented Reality === Developed using Apple ARKit and Google ARCore technology, Kubity Go's augmented reality feature maps the environment in a room detecting horizontal planes like tables and floors to track and place 3D objects. By blending digital objects and information with the environment, Kubity allows users to interact with 3D models in true augmented reality. Built-in communication features allows users to instantly share 3D models with anyone over text, email, social media, or direct device-to-device with a QR Code. Platform Support AR supports devices running iOS11 including: iPhone SE, iPhone 6s, iPhone 6s Plus, iPhone 7, iPhone 7 Plus, iPhone 8, iPhone X, all iPad Pro models, and iPad (2017). AR for Android requires Android 7.0 or later and access to the Google Play Store. === Virtual Reality === VR allows users to explore SketchUp models and Revit projects on-the-go right from a mobile device using Oculus Go, Google Cardboard, Samsung Gear VR, or the glasses-free Magic Window feature. Kubity's virtual reality feature is compatible with Oculus Go, Google Cardboard viewers and other cardboard compatible devices including clip-on style VR glasses like Homido Mini, as well as the mobile virtual reality headset, Samsung Gear VR. Samsung Gear VR supports: Galaxy S6, Galaxy S6 Edge, Galaxy S6 Edge+, Samsung Galaxy Note 5, Galaxy S7, Galaxy S7 Edge, Galaxy S8, Galaxy S8+, Samsung Galaxy Note Fan Edition, Samsung Galaxy Note 8, Samsung Galaxy A8/A8+ (2018), and Samsung Galaxy S9/Galaxy S9+. === Screen Mirroring === Screen mirroring allows a user to sync the sender device to a receiver on a webpage, then control from the sender device to give a remote presentation of the 3D model. Devices are easily synced by entering a six-digit number displayed on the receiving computer. == Platform support == On iOS, the Kubity application is compatible with devices running on the version 9.0 or higher. On Android, Kubity is compatible with devices running on the version 4.4 “Kit Kat” or higher. The web version of Kubity applications currently support web browsers compatible with WebGL2 : Mozilla Firefox and Google Chrome. AR is compatible with devices running iOS11 including: iPhone SE, iPhone 6s, iPhone 6s Plus, iPhone 7, iPhone 7 Plus, iPhone 8, iPhone X, all iPad Pro models, and iPad (2017), and Android devices. Requires Android 7.0 or later and access to the Google Play Store. VR is compatible with Google Cardboard viewers and other cardboard compatible devices including clip-on style VR glasses like Homido Mini, as well as the Samsung Gear VR and Oculus Go. Samsung Gear VR supports: Galaxy S6, Galaxy S6 Edge, Galaxy S6 Edge+, Samsung Galaxy Note 5, Galaxy S7, Galaxy S7 Edge, Galaxy S8, Galaxy S8+, Samsung Galaxy Note Fan Edition, Samsung Galaxy Note 8, Samsung Galaxy A8/A8+ (2018) and Samsung Galaxy S9/Galaxy S9+.

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  MIME Object Security Services

  MIME Object Security Services (MOSS) is a protocol that uses the multipart/signed and multipart/encrypted framework to apply digital signature and encryption services to MIME objects. == Details == The services are offered through the use of end-to-end cryptography between an originator and a recipient at the application layer. Asymmetric (public key) cryptography is used in support of the digital signature service and encryption key management. Symmetric (secret key) cryptography is used in support of the encryption service. The procedures are intended to be compatible with a wide range of public key management approaches, including both ad hoc and certificate-based schemes. Mechanisms are provided to support many public key management approaches. == Spreading == MOSS was never widely deployed and is now abandoned, largely due to the popularity of PGP.

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  Social television

  Social television is the union of television and social media. Millions of people now share their TV experience with other viewers on social media such as Twitter and Facebook using smartphones and tablets. TV networks and rights holders are increasingly sharing video clips on social platforms to monetise engagement and drive tune-in. The social TV market covers the technologies that support communication and social interaction around TV as well as companies that study television-related social behavior and measure social media activities tied to specific TV broadcasts – many of which have attracted significant investment from established media and technology companies. The market is also seeing numerous tie-ups between broadcasters and social networking players such as Twitter and Facebook. The market is expected to be worth $256bn by 2017. Social TV was named one of the 10 most important emerging technologies by the MIT Technology Review on Social TV in 2010. And in 2011, David Rowan, the editor of Wired magazine, named Social TV at number three of six in his peek into 2011 and what tech trends to expect to get traction. Ynon Kreiz, CEO of the Endemol Group told the audience at the Digital Life Design (DLD) conference in January 2011: "Everyone says that social television will be big. I think it's not going to be big—it's going to be huge". Much of the investment in the earlier years of social TV went into standalone social TV apps. The industry believed these apps would provide an appealing and complimentary consumer experience which could then be monetized with ads. These apps featured TV listings, check-ins, stickers and synchronised second-screen content but struggled to attract users away from Twitter and Facebook. Most of these companies have since gone out of business or been acquired amid a wave of consolidation and the market has instead focused on the activities of the social media channels themselves – such as Twitter Amplify, Facebook Suggested Videos and Snapchat Discover – and the technologies that support them. == Twitter == Twitter and Facebook are both helping users connect around media, which can provoke strong debate and engagement. Both social platforms want to be the 'digital watercooler' and host conversation around TV because the engagement and data about what media people consume can then be used to generate advertising revenue. As an open platform, conversation on Twitter is closely aligned with real-time events. In May 2013, it launched Twitter Amplify – an advertising product for media and consumer brands. With Amplify, Twitter runs video highlights from major live broadcasts, with advertisers' names and messages playing before the clip. By February 2014, all four major U.S. TV networks had signed up to the Amplify program, bringing a variety of premium TV content onto the social platform in the form of in-tweet real-time video clips. In June 2014, Twitter acquired its Twitter Amplify partner in the U.S. SnappyTV, a company that was helping broadcasters and rights holders to share video content both organically across social and via Twitter's Amplify program. Twitter continues to rely on Grabyo, which has also struck numerous deals with some of the largest broadcasters and rights holders in Europe and North America to share video content across Facebook and Twitter. == Facebook == Facebook made significant changes to its platform in 2014 including updates to its algorithm to enhance how it serves video in users' feeds. It also launched video autoplay to get users to watch the videos in their feeds. It rapidly surpassed Twitter and by the end of 2014 it was enjoying three billion video views a day on its platform and had announced a partnership with the NFL, one of Twitter's most active Twitter Amplify partners. In April 2015, at its F8 Developer Conference, it revealed it was working with Grabyo among other technology partners to bring video onto its platform. Then in July it announced it would be launching Facebook Suggested Videos, bringing related videos and ads to anyone that clicks on a video – a move that not only competed with Twitter's commercial video offering but also put it in direct competition with YouTube. == TV Time == TV Time is a television dedicated social network that allows users to keep track of the television series they watch, as well as films. It also allows them to express their reaction to the media they have seen with episode specific voting for favorite characters and emotional reaction to episodes, as well as commenting in episode restrictive pages. This way users are able to avoid spoilers while also finding a precise audience and community for each of their interactions, as opposed to bigger, non-television dedicated social medias such as Facebook and Twitter where the likelihood of unintentionally reading spoilers is much higher. TV Time offers an analytics service called "TVLytics" where the votes and reactions collected from users can be studied for research and television production purposes. == Advertising == According to Businessinsider.com, there are variety of applications for social TV, including support for TV ad sales, optimizing TV ad buys, making ad buys more efficient, as a complement to audience measurement, and eventually, audience forecasting and real-time optimization. Social TV data can ease access to focus groups and may create a positive feedback loop for generating ultra-sticky TV programming and multi-screen ad campaigns. == In numbers == Viewers share their TV experience on social media in real-time as events unfold: between 88-100m Facebook users login to the platform during the primetime hours of 8pm – 11pm in the US. The volume of social media engagement in TV is also rising – according to Nielsen SocialGuide, there was a 38% increase in tweets about TV in 2013 to 263m. For the 2014 Super Bowl, Twitter reported that a record 24.9 million tweets about the game were sent during the telecast, peaking at 381,605 tweets per minute. Facebook reported that 50 million people discussed the Super Bowl, generating 185 million interactions. The 2014 Oscars generated 5m tweets, viewed by an audience of 37m unique Twitter users and delivering 3.3bn impressions globally as conversation and key moments were shared virally across the platform. In 2014 the All England Lawn Tennis Club (AELTC), hosts of Wimbledon, used Grabyo to share video content across social. The videos were viewed 3.5 million times across Facebook and Twitter. In partnered with Grabyo again in 2015 and the videos generated over 48 million views across Facebook and Twitter. == Television shows with social integration == Here are some examples of how TV executives are integrating social elements with TV shows: C-SPAN streamed tweets from US Senators and Representatives during the quorum call The Voice had the judges of the program tweet during the show and the posts scrolls on the bottom of the screen. The use of Twitter also led to an increase in viewers. "Glee" Entertainment Weekly created a second screen viewing platform for the Glee season 3 premiere. == Related publications == Erika Jonietz. "Making TV Social, Virtually" MIT Technology Review. (January 11, 2010) AmigoTV (Alcatel-Lucent; Coppens et al.) – 2004 www.ist-ipmedianet.org/Alcatel_EuroiTV2004_AmigoTV_short_paper_S4-2.pdf Nextream (MIT Media Lab, Martin et al.) – 2010 Social Interactive Television: Immersive Shared Experiences and Perspectives (P. Cesar, D. Geerts, and K. Chorianopoulos (eds.)) – 2009 Social TV and the Emergence of Interactive TV – Multimedia Research Group – November 2010 Interactive Social TV on Service Oriented Environments: Challenges and Enablers (May 2011) == Systems == Boxee – acquired by Samsung GetGlue – acquired by i.TV Grabyo KIT digital Miso TV Tank Top TV WiO Xbox Live

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  Communications security

  Communications security is the discipline of preventing unauthorized interceptors from accessing telecommunications in an intelligible form, while still delivering content to the intended recipients. In the North Atlantic Treaty Organization culture, including United States Department of Defense culture, it is often referred to by the abbreviation COMSEC. The field includes cryptographic security, transmission security, emissions security and physical security of COMSEC equipment and associated keying material. COMSEC is used to protect both classified and unclassified traffic on military communications networks, including voice, video, and data. It is used for both analog and digital applications, and both wired and wireless links. Voice over secure internet protocol VOSIP has become the de facto standard for securing voice communication, replacing the need for Secure Terminal Equipment (STE) in much of NATO, including the U.S.A. USCENTCOM moved entirely to VOSIP in 2008. == Specialties == Cryptographic security: The component of communications security that results from the provision of technically sound cryptosystems and their proper use. This includes ensuring message confidentiality and authenticity. Emission security (EMSEC): The protection resulting from all measures taken to deny unauthorized persons information of value that might be derived from communications systems and cryptographic equipment intercepts and the interception and analysis of compromising emanations from cryptographic equipment, information systems, and telecommunications systems. Transmission security (TRANSEC): The component of communications security that results from the application of measures designed to protect transmissions from interception and exploitation by means other than cryptanalysis (e.g. frequency hopping and spread spectrum). Physical security: The component of communications security that results from all physical measures necessary to safeguard classified equipment, material, and documents from access thereto or observation thereof by unauthorized persons. == Related terms == ACES – Automated Communications Engineering Software AEK – Algorithmic Encryption Key AKMS – the Army Key Management System CCI – Controlled Cryptographic Item - equipment which contains COMSEC embedded devices CT3 – Common Tier 3 DTD – Data Transfer Device ICOM – Integrated COMSEC, e.g. a radio with built in encryption KEK – Key Encryption Key KG-30 – family of COMSEC equipment KOI-18 – Tape Reader General Purpose KPK – Key production key KYK-13 – Electronic Transfer Device KYX-15 – Electronic Transfer Device LCMS – Local COMSEC Management Software OTAR – Over the Air Rekeying OWK – Over the Wire Key SKL – Simple Key Loader SOI – Signal operating instructions STE – Secure Terminal Equipment (secure phone) STU-III – (obsolete secure phone, replaced by STE) TED – Trunk Encryption Device such as the WALBURN/KG family TEK – Traffic Encryption Key TPI – Two person integrity TSEC – Telecommunications Security (sometimes referred to in error transmission security or TRANSEC) Types of COMSEC equipment: Authentication equipment Crypto equipment: Any equipment that embodies cryptographic logic or performs one or more cryptographic functions (key generation, encryption, and authentication). Crypto-ancillary equipment: Equipment designed specifically to facilitate efficient or reliable operation of crypto-equipment, without performing cryptographic functions itself. Crypto-production equipment: Equipment used to produce or load keying material == DoD Electronic Key Management System == The Electronic Key Management System (EKMS) is a United States Department of Defense (DoD) key management, COMSEC material distribution, and logistics support system. The National Security Agency (NSA) established the EKMS program to supply electronic key to COMSEC devices in securely and timely manner, and to provide COMSEC managers with an automated system capable of ordering, generation, production, distribution, storage, security accounting, and access control. The Army's platform in the four-tiered EKMS, AKMS, automates frequency management and COMSEC management operations. It eliminates paper keying material, hardcopy Signal operating instructions (SOI) and saves the time and resources required for courier distribution. It has 4 components: LCMS provides automation for the detailed accounting required for every COMSEC account, and electronic key generation and distribution capability. ACES is the frequency management portion of AKMS. ACES has been designated by the Military Communications Electronics Board as the joint standard for use by all services in development of frequency management and crypto-net planning. CT3 with DTD software is in a fielded, ruggedized hand-held device that handles, views, stores, and loads SOI, Key, and electronic protection data. DTD provides an improved net-control device to automate crypto-net control operations for communications networks employing electronically keyed COMSEC equipment. SKL is a hand-held PDA that handles, views, stores, and loads SOI, Key, and electronic protection data. == Key Management Infrastructure (KMI) Program == KMI is intended to replace the legacy Electronic Key Management System to provide a means for securely ordering, generating, producing, distributing, managing, and auditing cryptographic products (e.g., asymmetric keys, symmetric keys, manual cryptographic systems, and cryptographic applications). This system is currently being fielded by Major Commands and variants will be required for non-DoD Agencies with a COMSEC Mission.

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  Automation integrator

  An automation integrator is a systems integrator company or individual who makes different versions of automation hardware and software work together, generally combining several subsystems to work together as one large system. The title may refer to those who only integrate hardware, although these will often work with software integrators. Software created by automation integrators allows devices to communicate with each other, as well as collecting and reporting data. The magazine Control Engineering publishes an annual “Automation Integrator Guide” which lists over 2,000 automation integrators. They also give an annual system integrator of the year award to three automation integration firms. The Control System Integrators Association (CSIA) maintains a buyers' guide of over 1200 member and nonmember systems integrators known as the Industrial Automation Exchange, or CSIA Exchange for short. == Certification == The Control System Integrators Association (CSIA) certifies automation integrators, through an audit based on 79 critical criteria from the best practices manual. Companies must be associate members of the CSIA to be eligible for certification. Integrators can also receive certification through a program launched in 2012 by the Robotics Industries Association. == Industries == Automation Integrators work in a wide variety of industries which use robotics and automation. Some of the most common include:

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  Tableau de Concordance

  The Tableau de Concordance was the main French diplomatic code used during World War I; the term also refers to any message sent using the code. It was a superenciphered four-digit code that was changed three times between 1 August 1914 and 15 January 1915. The Tableau de Concordance is considered superenciphered because there is more than one step required to use it. First, each word in a message is replaced by four digits via a codebook. These four digits are divided into three groups (one digit, two digits, one digit) so that when the whole message has been translated into code, the four-digit sets can be put together so it looks like the entire message is made up of two-digit pairs. This is called a "Straddle Gimmick." Then, in turn, each of these two digit pairs (and the single digits at the beginning and end) are replaced by two letters. The letters are then combined with no spaces for the final ciphertext. The manual for the Tableau de Concordance included the instruction that if there was not adequate time for completely enciphering the message, it should simply be sent in clear, because a partially enciphered message would have provided insight into the inner workings of the code.

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  Forking lemma

  The forking lemma is any of a number of related lemmas in cryptography research. The lemma states that if an adversary (typically a probabilistic Turing machine), on inputs drawn from some distribution, produces an output that has some property with non-negligible probability, then with non-negligible probability, if the adversary is re-run on new inputs but with the same random tape, its second output will also have the property. This concept was first used by David Pointcheval and Jacques Stern in "Security proofs for signature schemes," published in the proceedings of Eurocrypt 1996. In their paper, the forking lemma is specified in terms of an adversary that attacks a digital signature scheme instantiated in the random oracle model. They show that if an adversary can forge a signature with non-negligible probability, then there is a non-negligible probability that the same adversary with the same random tape can create a second forgery in an attack with a different random oracle. The forking lemma was later generalized by Mihir Bellare and Gregory Neven. The forking lemma has been used and further generalized to prove the security of a variety of digital signature schemes and other random-oracle based cryptographic constructions. == Statement of the lemma == The generalized version of the lemma is stated as follows. Let A be a probabilistic algorithm, with inputs (x, h1, ..., hq; r) that outputs a pair (J, y), where r refers to the random tape of A (that is, the random choices A will make). Suppose further that IG is a probability distribution from which x is drawn, and that H is a set of size h from which each of the hi values are drawn according to the uniform distribution. Let acc be the probability that on inputs distributed as described, the J output by A is greater than or equal to 1. We can then define a "forking algorithm" FA that proceeds as follows, on input x: Pick a random tape r for A. Pick h1, ..., hq uniformly from H. Run A on input (x, h1, ..., hq; r) to produce (J, y). If J = 0, then return (0, 0, 0). Pick h'J, ..., h'q uniformly from H. Run A on input (x, h1, ..., hJ−1, h'J, ..., h'q; r) to produce (J', y'). If J' = J and hJ ≠ h'J then return (1, y, y'), otherwise, return (0, 0, 0). Let frk be the probability that FA outputs a triple starting with 1, given an input x chosen randomly from IG. Then frk ≥ acc ⋅ ( acc q − 1 h ) . {\displaystyle {\text{frk}}\geq {\text{acc}}\cdot \left({\frac {\text{acc}}{q}}-{\frac {1}{h}}\right).} === Intuition === The idea here is to think of A as running two times in related executions, where the process "forks" at a certain point, when some but not all of the input has been examined. In the alternate version, the remaining inputs are re-generated but are generated in the normal way. The point at which the process forks may be something we only want to decide later, possibly based on the behavior of A the first time around: this is why the lemma statement chooses the branching point (J) based on the output of A. The requirement that hJ ≠ h'J is a technical one required by many uses of the lemma. (Note that since both hJ and h'J are chosen randomly from H, then if h is large, as is usually the case, the probability of the two values not being distinct is extremely small.) === Example === For example, let A be an algorithm for breaking a digital signature scheme in the random oracle model. Then x would be the public parameters (including the public key) A is attacking, and hi would be the output of the random oracle on its ith distinct input. The forking lemma is of use when it would be possible, given two different random signatures of the same message, to solve some underlying hard problem. An adversary that forges once, however, gives rise to one that forges twice on the same message with non-negligible probability through the forking lemma. When A attempts to forge on a message m, we consider the output of A to be (J, y) where y is the forgery, and J is such that m was the Jth unique query to the random oracle (it may be assumed that A will query m at some point, if A is to be successful with non-negligible probability). (If A outputs an incorrect forgery, we consider the output to be (0, y).) By the forking lemma, the probability (frk) of obtaining two good forgeries y and y' on the same message but with different random oracle outputs (that is, with hJ ≠ h'J) is non-negligible when acc is also non-negligible. This allows us to prove that if the underlying hard problem is indeed hard, then no adversary can forge signatures. This is the essence of the proof given by Pointcheval and Stern for a modified ElGamal signature scheme against an adaptive adversary. == Known issues with application of forking lemma == The reduction provided by the forking lemma is not tight. Pointcheval and Stern proposed security arguments for Digital Signatures and Blind Signature using Forking Lemma. Claus P. Schnorr provided an attack on blind Schnorr signatures schemes, with more than p o l y l o g ( n ) {\displaystyle polylog(n)} concurrent executions (the case studied and proven secure by Pointcheval and Stern). A polynomial-time attack, for Ω ( n ) {\displaystyle \Omega (n)} concurrent executions, was shown in 2020 by Benhamouda, Lepoint, Raykova, and Orrù. Schnorr also suggested enhancements for securing blind signatures schemes based on discrete logarithm problem.

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  Signals intelligence

  Signals intelligence (SIGINT) is the act and field of intelligence-gathering by interception of signals, whether communications between people (communications intelligence—abbreviated to COMINT) or from electronic signals not directly used in communication (electronic intelligence—abbreviated to ELINT). As classified and sensitive information is usually encrypted, signals intelligence may necessarily involve cryptanalysis (to decipher the messages). Traffic analysis—the study of who is signaling to whom and in what quantity—is also used to integrate information, and it may complement cryptanalysis. == History == === Origins === Electronic interceptions appeared as early as 1900, during the Boer War of 1899–1902. The British Royal Navy had installed wireless sets produced by Marconi on board their ships in the late 1890s, and the British Army used some limited wireless signalling. The Boers captured some wireless sets and used them to make vital transmissions. Since the British were the only people transmitting at the time, the British did not need special interpretation of the signals that they were. The birth of signals intelligence in a modern sense dates from the Russo-Japanese War of 1904–1905. As the Russian fleet prepared for conflict with Japan in 1904, the British ship HMS Diana stationed in the Suez Canal intercepted Russian naval wireless signals being sent out for the mobilization of the fleet, for the first time in history. === Development in World War I === Over the course of the First World War, a new method of signals intelligence reached maturity. Russia's failure to properly protect its communications fatally compromised the Russian Army's advance early in World War I and led to their disastrous defeat by the Germans under Ludendorff and Hindenburg at the Battle of Tannenberg. In 1918, French intercept personnel captured a message written in the new ADFGVX cipher, which was cryptanalyzed by Georges Painvin. This gave the Allies advance warning of the German 1918 Spring Offensive. The British in particular, built up great expertise in the newly emerging field of signals intelligence and codebreaking (synonymous with cryptanalysis). On the declaration of war, Britain cut all German undersea cables. This forced the Germans to communicate exclusively via either (A) a telegraph line that connected through the British network and thus could be tapped; or (B) through radio which the British could then intercept. Rear Admiral Henry Oliver appointed Sir Alfred Ewing to establish an interception and decryption service at the Admiralty; Room 40. An interception service known as 'Y' service, together with the post office and Marconi stations, grew rapidly to the point where the British could intercept almost all official German messages. The German fleet was in the habit each day of wirelessing the exact position of each ship and giving regular position reports when at sea. It was possible to build up a precise picture of the normal operation of the High Seas Fleet, to infer from the routes they chose where defensive minefields had been placed and where it was safe for ships to operate. Whenever a change to the normal pattern was seen, it immediately signalled that some operation was about to take place, and a warning could be given. Detailed information about submarine movements was also available. The use of radio-receiving equipment to pinpoint the location of any single transmitter was also developed during the war. Captain H.J. Round, working for Marconi, began carrying out experiments with direction-finding radio equipment for the army in France in 1915. By May 1915, the Admiralty was able to track German submarines crossing the North Sea. Some of these stations also acted as 'Y' stations to collect German messages, but a new section was created within Room 40 to plot the positions of ships from the directional reports. Room 40 played an important role in several naval engagements during the war, notably in detecting major German sorties into the North Sea. The battle of Dogger Bank was won in no small part due to the intercepts that allowed the Navy to position its ships in the right place. It played a vital role in subsequent naval clashes, including at the Battle of Jutland as the British fleet was sent out to intercept them. The direction-finding capability allowed for the tracking and location of German ships, submarines, and Zeppelins. The system was so successful that by the end of the war, over 80 million words, comprising the totality of German wireless transmission over the course of the war, had been intercepted by the operators of the Y-stations and decrypted. However, its most astonishing success was in decrypting the Zimmermann Telegram, a telegram from the German Foreign Office sent via Washington to its ambassador Heinrich von Eckardt in Mexico. === Postwar consolidation === With the importance of interception and decryption firmly established by the wartime experience, countries established permanent agencies dedicated to this task in the interwar period. In 1919, the British Cabinet's Secret Service Committee, chaired by Lord Curzon, recommended that a peace-time codebreaking agency should be created. The Government Code and Cypher School (GC&CS) was the first peace-time codebreaking agency, with a public function "to advise as to the security of codes and cyphers used by all Government departments and to assist in their provision", but also with a secret directive to "study the methods of cypher communications used by foreign powers". GC&CS officially formed on 1 November 1919, and produced its first decrypt on 19 October. By 1940, GC&CS was working on the diplomatic codes and ciphers of 26 countries, tackling over 150 diplomatic cryptosystems. The US Cipher Bureau was established in 1919 and achieved some success at the Washington Naval Conference in 1921, through cryptanalysis by Herbert Yardley. Secretary of War Henry L. Stimson closed the US Cipher Bureau in 1929 with the words "Gentlemen do not read each other's mail." === World War II === The use of SIGINT had even greater implications during World War II. The combined effort of intercepts and cryptanalysis for the whole of the British forces in World War II came under the code name "Ultra", managed from Government Code and Cypher School at Bletchley Park. Properly used, the German Enigma and Lorenz ciphers should have been virtually unbreakable, but flaws in German cryptographic procedures, and poor discipline among the personnel carrying them out, created vulnerabilities which made Bletchley's attacks feasible. Bletchley's work was essential to defeating the U-boats in the Battle of the Atlantic, and to the British naval victories in the Battle of Cape Matapan and the Battle of North Cape. In 1941, Ultra exerted a powerful effect on the North African desert campaign against German forces under General Erwin Rommel. General Sir Claude Auchinleck wrote that were it not for Ultra, "Rommel would have certainly got through to Cairo". Ultra decrypts featured prominently in the story of Operation SALAM, László Almásy's mission across the desert behind Allied lines in 1942. Prior to the Normandy landings on D-Day in June 1944, the Allies knew the locations of all but two of Germany's fifty-eight Western Front divisions. Winston Churchill was reported to have told King George VI: "It is thanks to the secret weapon of General Menzies, put into use on all the fronts, that we won the war!" Supreme Allied Commander, Dwight D. Eisenhower, at the end of the war, described Ultra as having been "decisive" to Allied victory. Official historian of British Intelligence in World War II Sir Harry Hinsley argued that Ultra shortened the war "by not less than two years and probably by four years"; and that, in the absence of Ultra, it is uncertain how the war would have ended. At a lower level, German cryptanalysis, direction finding, and traffic analysis were vital to Rommel's early successes in the Western Desert Campaign until British forces tightened their communications discipline and Australian raiders destroyed his principal SIGINT Company. == Technical definitions == The United States Department of Defense has defined the term "signals intelligence" as: A category of intelligence comprising either individually or in combination all communications intelligence (COMINT), electronic intelligence (ELINT), and foreign instrumentation signals intelligence (FISINT), however transmitted. Intelligence derived from communications, electronic, and foreign instrumentation signals. Being a broad field, SIGINT has many sub-disciplines. The two main ones are communications intelligence (COMINT) and electronic intelligence (ELINT). == Disciplines shared across the branches == === Targeting === A collection system has to know to look for a particular signal. "System", in this context, has several nuances. Targeting is the process of developing collection requirements: "1. A

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  Automation integrator

  An automation integrator is a systems integrator company or individual who makes different versions of automation hardware and software work together, generally combining several subsystems to work together as one large system. The title may refer to those who only integrate hardware, although these will often work with software integrators. Software created by automation integrators allows devices to communicate with each other, as well as collecting and reporting data. The magazine Control Engineering publishes an annual “Automation Integrator Guide” which lists over 2,000 automation integrators. They also give an annual system integrator of the year award to three automation integration firms. The Control System Integrators Association (CSIA) maintains a buyers' guide of over 1200 member and nonmember systems integrators known as the Industrial Automation Exchange, or CSIA Exchange for short. == Certification == The Control System Integrators Association (CSIA) certifies automation integrators, through an audit based on 79 critical criteria from the best practices manual. Companies must be associate members of the CSIA to be eligible for certification. Integrators can also receive certification through a program launched in 2012 by the Robotics Industries Association. == Industries == Automation Integrators work in a wide variety of industries which use robotics and automation. Some of the most common include:

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  Data monetization

  Data monetization, a form of monetization, may refer to the act of generating measurable economic benefits from available data sources (analytics). Less commonly, it may also refer to the act of monetizing data services. In the case of analytics, typically, these benefits accrue as revenue or expense savings, but may also include market share or corporate market value gains. Data monetization leverages data generated through business operations, available exogenous data or content, as well as data associated with individual actors such as that collected via electronic devices and sensors participating in the internet of things. For example, the ubiquity of the internet of things is generating location data and other data from sensors and mobile devices at an ever-increasing rate. When this data is collated against traditional databases, the value and utility of both sources of data increases, leading to tremendous potential to mine data for social good, research and discovery, and achievement of business objectives. Closely associated with data monetization are the emerging data as a service models for transactions involving data by the data item. There are three ethical and regulatory vectors involved in data monetization due to the sometimes conflicting interests of actors involved in the digital supply chain. The individual data creator who generates files and records through his own efforts or owns a device such as a sensor or a mobile phone that generates data has a claim to ownership of data. The business entity that generates data in the course of its operations, such as its transactions with financial institutions or risk factors discovered through feedback from customers also has a claim on data captured through their systems and platforms. However, the person that contributed the data may also have a legitimate claim on the data. Internet platforms and service providers, such as Google or Facebook that require a user to forgo some ownership interest in their data in exchange for use of the platform also have a legitimate claim on the data. Thus the practice of data monetization, although common since 2000, is now getting increasing attention from regulators. The European Union and the United States Congress have begun to address these issues. For instance, in the financial services industry, regulations involving data are included in the Gramm–Leach–Bliley Act and Dodd-Frank. Some individual creators of data are shifting to using personal data vaults and implementing vendor relationship management concepts as a reflection of an increasing resistance to their data being federated or aggregated and resold without compensation. Groups such as the Personal Data Ecosystem Consortium, Patient privacy rights, and others are also challenging corporate cooptation of data without compensation. Financial services companies are a relatively good example of an industry focused on generating revenue by leveraging data. Credit card issuers and retail banks use customer transaction data to improve targeting of cross-sell offers. Partners are increasingly promoting merchant based reward programs which leverage a bank’s data and provide discounts to customers at the same time. == Types of data monetization == Internal data monetization - An organization's data is used internally, resulting in economic benefit. This is commonly the case in organizations using analytics to uncover insights, resulting in improved profit, cost savings or the avoidance of risk. Internal data monetization is currently the most common form of monetization, requiring far fewer security, intellectual property, and legal precautions when compared to other types. The potential economic gains from this type of data monetization are limited by the organization's internal structure and situation. External data monetization - A person or organization makes data they possess available on a for-fee basis to external parties, or as a broker for same. This type of monetization is less common and requires various methods to distribute the data to potential buyers and consumers. However, the economic gain that results from collecting data, packaging and distributing it, can be quite large. == Steps == Identification of available data sources – this includes data currently available for monetization as well as other external data sources that may enhance the value of what’s currently available. Connect, aggregate, attribute, validate, authenticate, and exchange data - this allows data to be converted directly into actionable or revenue generating insight or services. Set terms and prices and facilitate data trading - methods for data vetting, storage, and access. For example, many global corporations have locked and siloed data storage infrastructures, which hinders efficient access to data and cooperative and real-time exchange. Perform Research and analytics – draw predictive insights from existing data as a basis for using data for to reduce risk, enhance product development or performance, or improve customer experience or business outcomes. Action and leveraging – the last phase of monetizing data includes determining alternative or improved data centric products, ideas, or services. Examples may include real-time actionable triggered notifications or enhanced channels such as web or mobile response mechanisms. == Pricing variables and factors == A fee for use of a platform to connect buyers and sellers use of a platform to configure, organize, and otherwise process data included in a data trade connecting or including a device or sensor into a data supply chain connecting and credentialing a creator of a data source and a data buyer – often through a federated identity connecting a data source to other data sources to be included in a data supply chain use of an internet service or other transmission services for uploading and downloading data – sometimes, for an individual, through a personal cloud use of encrypted keys to achieve secure data transfer use of a search algorithm specifically designed to tag data sources that contain data points of value to the data buyer linking a data creator or generator to a data collection protocol or form server actions – such as a notification – triggered by an update to a data item or data source included in a data supply chain A price or exchange or other trade value assigned by a data creator or generator to a data item or a data source offered by a data buyer to a data creator assigned by a data buyer for a data item or a data source formatted according to criteria set by a data buyer An incremental fee assigned by a data buyer for a data item or a data set scaled to the reputation of the data creator == Benefits == Improved decision-making that leads to real time crowd sourced research, improved profits, decreased costs, reduced risk and improved compliance More impactful decisions (e.g., make real-time decisions) More timely (lower latency) decisions (e.g., a vendor making purchase recommendations while the customer is still on the phone or in the store, a customer connecting with multiple vendors to discover the best price, triggered notifications when thresholds are reached for data values) More granular decisions (e.g., localized pricing decisions at an individual or device or sensor level versus larger aggregates). Targeted Marketing (e.g., Vendors with access to big data can make targeted advertisements to specific customers within a set data pool decreasing costs for the advertiser and reaching most interested customers) == Frameworks == There are a wide variety of industries, firms and business models related to data monetization. The following frameworks have been offered to help understand the types of business models that are used: Roger Ehrenberg of IA Ventures, a venture capital firm that invests in this sector, has defined three basic types of data product firms: Contributory databases. The magic of these businesses is that a customer provides their own data in exchange for receiving a more robust set of aggregated data back that provides insight into the broader marketplace, or provides a vehicle for expressing a view. Give a little, get a lot back in return – a pretty compelling value proposition, and one that frequently results in a payment from the data contributor in exchange for receiving enriched, aggregated data. Once these contributory databases are developed and customers become reliant on their insights, they become extremely valuable and persistent data assets. Data processing platforms. These businesses create barriers through a combination of complex data architectures, proprietary algorithms, and rich analytics to help customers consume data in whatever form they please. Often these businesses have special relationships with key data providers, that when combined with other data and processed as a whole create valuable differentiation and competitive barriers. Bloomberg is an example of a powerful

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  Correlation immunity

  In mathematics, the correlation immunity of a Boolean function is a measure of the degree to which its outputs are uncorrelated with some subset of its inputs. Specifically, a Boolean function is said to be correlation-immune of order m if every subset of m or fewer variables in x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\ldots ,x_{n}} is statistically independent of the value of f ( x 1 , x 2 , … , x n ) {\displaystyle f(x_{1},x_{2},\ldots ,x_{n})} . == Definition == A function f : F 2 n → F 2 {\displaystyle f:\mathbb {F} _{2}^{n}\rightarrow \mathbb {F} _{2}} is k {\displaystyle k} -th order correlation immune if for any independent n {\displaystyle n} binary random variables X 0 … X n − 1 {\displaystyle X_{0}\ldots X_{n-1}} , the random variable Z = f ( X 0 , … , X n − 1 ) {\displaystyle Z=f(X_{0},\ldots ,X_{n-1})} is independent from any random vector ( X i 1 … X i k ) {\displaystyle (X_{i_{1}}\ldots X_{i_{k}})} with 0 ≤ i 1 < … < i k < n {\displaystyle 0\leq i_{1}<\ldots Read more →