AI Detector Accuracy

AI Detector Accuracy — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Universal portfolio algorithm

    Universal portfolio algorithm

    The universal portfolio algorithm is a portfolio selection algorithm from the field of machine learning and information theory. The algorithm learns adaptively from historical data and maximizes log-optimal growth rate in the long run, per the Kelly criterion. It was introduced by the late Stanford University information theorist Thomas M. Cover. The algorithm rebalances the portfolio at the beginning of each trading period. At the beginning of the first trading period it starts with a naive diversification. In the following trading periods the portfolio composition depends on the historical total return of all possible constant-rebalanced portfolios. The universal portfolio algorithm is the predecessor of the various online portfolio selection methodologies.

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  • Non-personal data

    Non-personal data

    Non-Personal Data (NPD) is electronic data that does not contain any information that can be used to identify a natural person. Thus, it can either be data that has no personal information to begin with (such as weather data, stock prices, data from anonymous IoT sensors); or it is data that had personal data that was subsequently pseudoanonymized (for example, identifiable strings substituted with random strings) or anonymized (such as by irreversibly removing all personal data). NPD is part of the overall Data Governance Strategy of a region or country. While personal data are covered by Data Protection Legislation such as GDPR, other kinds of data would fall under the scope of NPD Regulation. == Importance of non-personal data == It has been pointed out that the future is data-driven. What this means is that much of the present innovation taking place in domains such as Machine Learning and Artificial Intelligence is fueled by data, which is needed for calibrating the complex models (comprising neural network-based as well as other kinds). The larger the volume, diversity and quality of the data, the higher is the quality of the model, leading to better predictions and explanations. However, there is a flip-side to data availability. The newly-emerging awareness of privacy and the consequent need for powerful Data Protection Regulations (such as GDPR) makes it increasingly difficult or impossible to obtain data in the quantities required. This is a contradiction, and the only way out would be to remove all personal data from data sets (either by Data anonymization or Pseudonymization coupled with noise injection, at which point it becomes NPD. Therefore, many innovation-friendly countries are coming out with regulatory regimes that would ensure that personal data is protected, while, at the same time, non-personal data can be extracted from personal data so that innovation is fostered. In other words, NPD 'unlocks' value that was locked away in data sets that have personally-identifiable information. It is expected that multiple NPD data sets will begin to be available on free or commercial basis from different providers once the regulations are in place. == Emerging regulatory frameworks == Non-Personal Data has significant uses that may be economic, social, political or security-related. Several countries and regions are in the process of regulating the use of NPD. In May 2019, the European Union operationalized its Regulation of the Free Flow of NPD. India announced a nine-member expert committee to make recommendations on the regulation of NPD in 2019, which published its first report in mid-2020. The report was opened for public comments, after which it was revised and published in December 2020. == Proposed NPD regulatory framework in India == The following were the objectives of the proposed Indian regulation as per the revised report: Sovereignty: India has rights over the data of India, its people and organisations. Benefit India: Benefits of data must accrue to India and its people. Benefits the world: Innovation, new models and algorithms for the world. Privacy: Misuse, reidentification and harms must be prevented. Simplicity: The regulations should be simple, digital and unambiguous. Innovation and entrepreneurship: The data should be freely available for innovation and entrepreneurship in India. == Concerns == The major concern in the use of NPD is if there are techniques (statistical or AI-based) by which multiple data sets can be used to extract personally-identifiable data.

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  • SQL/PSM

    SQL/PSM

    SQL/PSM (SQL/Persistent Stored Modules) is an ISO standard mainly defining an extension of SQL with a procedural language for use in stored procedures. Initially published in 1996 as an extension of SQL-92 (ISO/IEC 9075-4:1996, a version sometimes called PSM-96 or even SQL-92/PSM), SQL/PSM was later incorporated into the multi-part SQL:1999 standard, and has been part 4 of that standard since then, most recently in SQL:2023. The SQL:1999 part 4 covered less than the original PSM-96 because the SQL statements for defining, managing, and invoking routines were actually incorporated into part 2 SQL/Foundation, leaving only the procedural language itself as SQL/PSM. The SQL/PSM facilities are still optional as far as the SQL standard is concerned; most of them are grouped in Features P001-P008. SQL/PSM standardizes syntax and semantics for control flow, exception handling (called "condition handling" in SQL/PSM), local variables, assignment of expressions to variables and parameters, and (procedural) use of cursors. It also defines an information schema (metadata) for stored procedures. SQL/PSM is one language in which methods for the SQL:1999 structured types can be defined. The other is Java, via SQL/JRT. SQL/PSM is derived, seemingly directly, from Oracle's PL/SQL. Oracle developed PL/SQL and released it in 1991, basing the language on the US Department of Defense's Ada programming language. However, Oracle has maintained a distance from the standard in its documentation. IBM's SQL PL (used in DB2) and Mimer SQL's PSM were the first two products officially implementing SQL/PSM. It is commonly thought that these two languages, and perhaps also MySQL/MariaDB's procedural language, are closest to the SQL/PSM standard. However, a PostgreSQL addon implements SQL/PSM (alongside its other procedural languages like the PL/SQL-derived plpgsql), although it is not part of the core product. RDF functionality in OpenLink Virtuoso was developed entirely through SQL/PSM, combined with custom datatypes (e.g., ANY for handling URI and Literal relation objects), sophisticated indexing, and flexible physical storage choices (column-wise or row-wise).

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  • Anyword

    Anyword

    Anyword is a technology company that offers an artificial intelligence platform, using natural language processing to generate and optimize marketing text for websites, social media, email, and ads. The company also offers a complete managed service to publishers and brands to help them increase their revenue through social ads. It is used by National Geographic, Red Bull, The New York Times, BBC, Ted Baker, etc. The company has an office in New York, and Tel Aviv. == History == It was founded in 2013 — its original name was Keywee Inc. In March 2015, Anyword received $9.1 million in the Series A funding round led by a notable group of investors. In July 2016, the company was selected as an official Facebook Marketing Partner. In August 2019, Anyword was named Best Content Marketing Platform in the Digiday Technology Award winners. In November 2021, it raised $21 million in its Series B funding round.

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  • RCUDA

    RCUDA

    rCUDA, which stands for Remote CUDA, is a type of middleware software framework for remote GPU virtualization. Fully compatible with the CUDA application programming interface (API), it allows the allocation of one or more CUDA-enabled GPUs to a single application. Each GPU can be part of a cluster or running inside of a virtual machine. The approach is aimed at improving performance in GPU clusters that are lacking full utilization. GPU virtualization reduces the number of GPUs needed in a cluster, and in turn, leads to a lower cost configuration – less energy, acquisition, and maintenance. The recommended distributed acceleration architecture is a high performance computing cluster with GPUs attached to only a few of the cluster nodes. When a node without a local GPU executes an application needing GPU resources, remote execution of the kernel is supported by data and code transfers between local system memory and remote GPU memory. rCUDA is designed to accommodate this client-server architecture. On one end, clients employ a library of wrappers to the high-level CUDA Runtime API, and on the other end, there is a network listening service that receives requests on a TCP port. Several nodes running different GPU-accelerated applications can concurrently make use of the whole set of accelerators installed in the cluster. The client forwards the request to one of the servers, which accesses the GPU installed in that computer and executes the request in it. Time-multiplexing the GPU, or in other words sharing it, is accomplished by spawning different server processes for each remote GPU execution request. == rCUDA v20.07 == The rCUDA middleware enables the concurrent usage of CUDA-compatible devices remotely. rCUDA employs either the InfiniBand network or the socket API for the communication between clients and servers. rCUDA can be useful in three different environments: Clusters. To reduce the number of GPUs installed in High Performance Clusters. This leads to energy savings, as well as other related savings like acquisition costs, maintenance, space, cooling, etc. Academia. In commodity networks, to offer access to a few high performance GPUs concurrently to many students. Virtual Machines. To enable the access to the CUDA facilities on the physical machine. The current version of rCUDA (v20.07) supports CUDA version 9.0, excluding graphics interoperability. rCUDA v20.07 targets the Linux OS (for 64-bit architectures) on both client and server sides. CUDA applications do not need any change in their source code in order to be executed with rCUDA.

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  • AI notetaker

    AI notetaker

    An AI notetaker is a tool using artificial intelligence to take notes during meetings. They are created by tech companies such as Microsoft and Google; by AI transcription services such Otter.ai, and by smaller firms such as Cluely and Krisp. Some business executives send AI notetakers to attend meetings not only to take notes, but also to answer questions on their behalf. The use of AI notetakers raises ethical questions, including recording meetings without the consent of all participants and the possibility that the notetaker will hallucinate and misrepresent what was said during meetings. There are also concerns when it comes to the privacy and security of meeting data and the sensitive information that lives inside meetings. Further controversies have developed from the use of AI notetakers such as Cluely to cheat in technical job interviews. == Technology == Large technology companies have integrated transcription capabilities into broader productivity and accessibility tools, including real-time captioning, dictation, and meeting documentation features embedded in operating systems and office platforms. Standalone transcription platforms, such as Transkriptor, focus specifically on automated transcription workflows and apply AI-based speech recognition to convert audio and video recordings into text. The software supports transcription in multiple languages and processes recordings uploaded via a web interface as well as through mobile and browser extensions. Tools of this type typically provide editable, time-aligned transcripts and export options for text and subtitle formats, cloud-based processing, multilingual support, and automation in transcription technology.

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  • Data janitor

    Data janitor

    A data janitor is a person who works to take big data and condense it into useful amounts of information. Also known as a "data wrangler", a data janitor sifts through data for companies in the information technology industry. A multitude of start-ups rely on large amounts of data, so a data janitor works to help these businesses with this basic, but difficult process of interpreting data. While it is a commonly held belief that data janitor work is fully automated, many data scientists are employed primarily as data janitors. The information technology industry has been increasingly turning towards new sources of data gathered on consumers, so data janitors have become more commonplace in recent years.

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  • MaPS S.A.

    MaPS S.A.

    MaPS S.A. is a software editor founded in 2011 by Thierry Muller. The company is headquartered in Luxembourg. Its platform, called MaPS System, provides Data Management software for Multichannel Marketing. == History and funding == The first version of MaPS System was released under the agency Prem1um S.A. in 2005 in the partnership with Pingroom agency. In combination with MaPS System, Prem1um also provided various consulting services in Marketing, Publishing and Sales. This is where MaPS System takes its names (M stands for Marketing, P for Publishing and S for Sales). In 2011, after being successful, Prem1um S.A. decided to enable the software MaPS System to operate independently under MaPS S.A., as a separate company and editor of the software. The first financial supports were provided by Malta ICI, a Venture Capital firm, and the local partner Chameleon Invest, a seed-capital fund led by Business Angels, who invested €900,000. In a second investment round in 2014 led by Newion Investments, a Venture Capital firm, €1.4 Million were raised, thus amounting to total assets of €2.2 Million. In 2016, the company was taken over by three private investors. In 2018, after two years of continuous growth and European expansion in Belgium, Germany and Switzerland, MaPS S.A acquired Awevo, an e-commerce web agency. == Products == The services included in MaPS System range from the data centralization, Data Governance to an optimized Multichannel Marketing. The software currently includes more than 35 modules for Master Data Management, Product Information Management, Digital Asset Management, Business Process Management including catalogue Publishing features. == Certifications == In 2019, MaPS System and Awevo received "Made in Luxembourg" label, given to the companies whose services are entirely designed in Luxembourg, without any production or development offshoring. MaPS System is a member of ICT Cluster by Luxinnovation.

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  • Random (software)

    Random (software)

    Random was an iOS mobile app that used algorithms and human-curation to create an adaptive interface to the Internet. The app served a remix of relevance and serendipity that allowed people to find diverse topics and interesting content that they might not have encountered otherwise. Random did not require a login or sign-up - the use of the app was anonymous. The app was powered by an artificial intelligence that learned from direct and indirect user interactions inside the app. While learning and adapting to a person, Random created a unique anonymous choice profile that was then used for recommending topics and content. The app didn't recommend the same content twice. == User interface == Random's user interface was made of ever-changing topic blocks that contained keywords and images. By choosing any of the blocks, the user would see related web content. By closing the web content, the user could access new related topics. The user interface allowed people to get more information about a specific topic area or then just leap freely from topic to topic. The content recommended by Random could be any type of web content, varying from news articles to long-form stories and from photographs to videos. Every user of the Random was curating content for other users by using the app. == History == Random was launched in March 2014. The startup was backed by Skype co-founder Janus Friis. The Random app received a strong reception from the likes of The New York Times, TechCrunch, New Scientist, Vice, and other leading publications. The app went on to gain traction with an active and loyal user community of several hundreds of thousands. This was not enough to support the free app model the team strongly believed in, and the service was terminated in December 2015. == Reception == Various reviews in media have emphasized that Random enables people to break their filter bubble and find diverse content they might not find elsewhere. Alan Henry of Lifehacker wrote: "Random... breaks you out by intentionally guiding you to new topics and interesting articles at sites you may not otherwise read." Vice Motherboard's Claire Evans says that: "Random never turns into a filter bubble, because it perpetually injects the irrational into my experience… in a cocktail of relevancy and serendipity." The app has been said to have a unique, minimalistic user experience. Kit Eaton of The New York Times commented that Random "let's you browse the news in a different way to all the other news sites you've probably ever used." Mashable reviewed Random by concluding that the "app may be one of the most simple content-discovery apps on the market."

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  • Jump-and-Walk algorithm

    Jump-and-Walk algorithm

    Jump-and-Walk is an algorithm for point location in triangulations (though most of the theoretical analysis were performed in 2D and 3D random Delaunay triangulations). Surprisingly, the algorithm does not need any preprocessing or complex data structures except some simple representation of the triangulation itself. The predecessor of Jump-and-Walk was due to Lawson (1977) and Green and Sibson (1978), which picks a random starting point S and then walks from S toward the query point Q one triangle at a time. But no theoretical analysis was known for these predecessors until after mid-1990s. Jump-and-Walk picks a small group of sample points and starts the walk from the sample point which is the closest to Q until the simplex containing Q is found. The algorithm was a folklore in practice for some time, and the formal presentation of the algorithm and the analysis of its performance on 2D random Delaunay triangulation was done by Devroye, Mucke and Zhu in mid-1990s (the paper appeared in Algorithmica, 1998). The analysis on 3D random Delaunay triangulation was done by Mucke, Saias and Zhu (ACM Symposium of Computational Geometry, 1996). In both cases, a boundary condition was assumed, namely, Q must be slightly away from the boundary of the convex domain where the vertices of the random Delaunay triangulation are drawn. In 2004, Devroye, Lemaire and Moreau showed that in 2D the boundary condition can be withdrawn (the paper appeared in Computational Geometry: Theory and Applications, 2004). Jump-and-Walk has been used in many famous software packages, e.g., QHULL, Triangle and CGAL.

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  • Information and media literacy

    Information and media literacy

    Information and media literacy (IML) is a combination of information literacy and media literacy. It enables people to show and make informed judgments as users of information and media, as well as to become skillful creators and producers of information and media messages. The transformative nature of IML includes creative works and creating new knowledge; to publish and collaborate responsibly requires ethical, cultural and social understanding. IML is also known as media and information literacy (MIL). UNESCO first adopted the term MIL in 2008 as a "composite concept" combining the competencies of information literacy and media literacy. UNESCO emphasizes the importance of global education in media and information literacy, and in 2013 defined Media and Information Literacy (MIL) as the ability to access, evaluate, use, and create information and media content in critical and ethical ways. Prior to the 1990s, the primary focus of information literacy was research skills. Media literacy, a study that emerged around the 1970s, traditionally focuses on the analysis and the delivery of information through various forms of media. Information literacy, as a skill proposed as early as 1974, centers on an individual's ability to recognize information needs and effectively locate, evaluate, and use information. These days, the study of information literacy has been extended to include the study of media literacy in many countries like the UK, Australia and New Zealand. It is also referred to as information and communication technologies (ICT) in the United States. Educators such as Gregory Ulmer have also defined the field as electracy.Media literacy is the ability to actively inquire into and think critically about information. It includes the ability to understand, evaluate, and create media content, and is an essential skill in today's information society. Livingstone, Van Couvering, and Thumim (2008) described the distinction between media literacy and information literacy: "Media literacy views media as lenses or windows for observing the world and expressing the self, whereas information literacy sees information as a tool for taking action in the world." == Integration of media and information literacy == Historically, the fields of information and media literacy have been separate, but over the course of the 21st century there have been calls to integrate both fields. Most definitions of information and media literacy include not only the abilities to locate, access, and analyze information but also the ability to create information. Only by integrating media literacy with information literacy can students better understand the sources of information and how it is used. Media education has primarily taken place in educational institutions, while information education has primarily occurred in libraries. Discussions surrounding the overlap of information literacy and media literacy came to fruition in the mid-to-late 2000s and 2010s as noted by Marcus Leaning. == In the digital age == The definition of literacy is "the ability to read and write". In practice many more skills are needed to locate, critically assess and make effective use of information. By extension, literacy now also includes the ability to manage and interact with digital information and media, in personal, shared and public domains. Historically, "information literacy" has largely been seen from the relatively top-down, organisational viewpoint of library and information sciences. However the same term is also used to describe a generic "information literacy" skill. The modern digital age has led to the proliferation of information spread across the Internet. Individuals must be able to recognize whether information is true or false and better yet know how to locate, evaluate, use, and communicate information in various formats; this is called information literacy. Towards the end of the 20th century, literacy was redefined to include "new literacies" relating to the new skills needed in everyday experience. "Multiliteracies" recognised the multiplicity of literacies, which were often used in combination. "21st century skills" frameworks link new literacies to wider life skills such as creativity, critical thinking, accountability. What these approaches have in common is a focus on the multiple skills needed by individuals to navigate changing personal, professional and public "information landscapes". As the conventional definition of literacy itself continues to evolve among practitioners, so too has the definition of information literacies. Noteworthy definitions include: Zurkowski defined information literacy as "the ability to find known or knowable content on any subject." CILIP, the Chartered Institute of Library and Information Practitioners, defines information literacy as "the ability to think critically and make balanced judgements about any information we find and use". In the United States, the definition proposed by the Association of College and Research Libraries (ACRL) is the most widely recognized. It defines information literacy as "a set of abilities requiring individuals to recognize when information is needed and to locate, evaluate, and use the needed information effectively." JISC, the Joint Information Systems Committee, refers to information literacy as one of six "digital capabilities", seen as an interconnected group of elements centered on "ICT literacy". Mozilla groups digital and other literacies as "21st century skills", a "broad set of knowledge, skills, habits and traits that are important to succeed in today's world". UNESCO, the United Nations Educational, Scientific and Cultural Organization, recognizing the necessity of teaching and learning both traditional and new types of information, the global importance of education was emphasized in 2008 through the "Teacher Media and Information Literacy (MIL) Curriculum". It defines MIL as a set of competencies that enable citizens to access, retrieve, understand, evaluate, use, create, and share information and media content in all formats through various tools in a critical, ethical, and effective manner, so as to participate in and carry out personal, professional, and social activities. Besides this, UNESCO also asserts information literacy as a "universal human right". == 21st-century students == In modern society, although the overall level of education has improved, the channels for knowledge production and dissemination have become increasingly diverse and commercialized, and traditional authoritative institutions no longer hold a monopoly over knowledge validation. While digital platforms have broadened access to information, they have also weakened trust mechanisms and evaluation standards, making epistemological skepticism a norm. Moreover, with the rise and spread of social media, misinformation and disinformation can be just as easily accessed in both densely and sparsely populated areas. These factors further underscore the importance of information literacy education. The IML learning capacities prepare students to be 21st century literate. According to Jeff Wilhelm (2000), "technology has everything to do with literacy. And being able to use the latest electronic technologies has everything to do with being literate." He supports his argument with J. David Bolter's statement that "if our students are not reading and composing with various electronic technologies, then they are illiterate. They are not just unprepared for the future; they are illiterate right now, in our current time and context". In a broader sense, developing this advanced competency of media and information literacy is essential, as it is crucial for students to exercise their freedom of expression in the 21st century. Wilhelm's statement is supported by the 2005 Wired World Phase II (YCWW II) survey conducted by the Media Awareness Network of Canada on 5000 Grade 4 – 11 students. The key findings of the survey were: 62% of Grade 4 students prefer the Internet. 38% of Grade 4 students prefer the library. 91% of Grade 11 students prefer the Internet. 9% of Grade 11 students prefer the library. Marc Prensky (2001) uses the term "digital native" to describe people who have been brought up in a digital world. The Internet has been a pervasive element of young people's home lives. 94% of kids reported that they had Internet access at home, and a significant majority (61%) had a high-speed connection. By the time kids reach Grade 11, half of them (51 percent) have their own Internet-connected computer, separate and apart from the family computer. The survey also showed that young Canadians are now among the most wired in the world. Contrary to the earlier stereotype of the isolated and awkward computer nerd, today's wired kid is a social kid. In general, many students are better networked through the use of technology than most teachers and parents, who may not understand the abilities of technology.

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  • OpenSMILE

    OpenSMILE

    openSMILE is source-available software for automatic extraction of features from audio signals and for classification of speech and music signals. "SMILE" stands for "Speech & Music Interpretation by Large-space Extraction". The software is mainly applied in the area of automatic emotion recognition and is widely used in the affective computing research community. The openSMILE project exists since 2008 and is maintained by the German company audEERING GmbH since 2013. openSMILE is provided free of charge for research purposes and personal use under a source-available license. For commercial use of the tool, the company audEERING offers custom license options. == Application Areas == openSMILE is used for academic research as well as for commercial applications in order to automatically analyze speech and music signals in real-time. In contrast to automatic speech recognition which extracts the spoken content out of a speech signal, openSMILE is capable of recognizing the characteristics of a given speech or music segment. Examples for such characteristics encoded in human speech are a speaker's emotion, age, gender, and personality, as well as speaker states like depression, intoxication, or vocal pathological disorders. The software further includes music classification technology for automatic music mood detection and recognition of chorus segments, key, chords, tempo, meter, dance-style, and genre. The openSMILE toolkit serves as benchmark in manifold research competitions such as Interspeech ComParE, AVEC, MediaEval, and EmotiW. == History == The openSMILE project was started in 2008 by Florian Eyben, Martin Wöllmer, and Björn Schuller at the Technical University of Munich within the European Union research project SEMAINE. The goal of the SEMAINE project was to develop a virtual agent with emotional and social intelligence. In this system, openSMILE was applied for real-time analysis of speech and emotion. The final SEMAINE software release is based on openSMILE version 1.0.1. In 2009, the emotion recognition toolkit (openEAR) was published based on openSMILE. "EAR" stands for "Emotion and Affect Recognition". In 2010, openSMILE version 1.0.1 was published and was introduced and awarded at the ACM Multimedia Open-Source Software Challenge. Between 2011 and 2013, the technology of openSMILE was extended and improved by Florian Eyben and Felix Weninger in the context of their doctoral thesis at the Technical University of Munich. The software was also applied for the project ASC-Inclusion, which was funded by the European Union. For this project, the software was extended by Erik Marchi in order to teach emotional expression to autistic children, based on automatic emotion recognition and visualization. In 2013, the company audEERING acquired the rights to the code-base from the Technical University of Munich and version 2.0 was published under a source-available research license. Until 2016, openSMILE was downloaded more than 50,000 times worldwide and has established itself as a standard toolkit for emotion recognition. == Awards == openSMILE was awarded in 2010 in the context of the ACM Multimedia Open Source Competition. The software tool is applied in numerous scientific publications on automatic emotion recognition. openSMILE and its extension openEAR have been cited in more than 1000 scientific publications until today.

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  • LiveChat

    LiveChat

    LiveChat is an AI customer service software with chatbot, online chat, help desk software, and web analytics capabilities. LiveChat is used by over 76,000 companies. It was first launched in 2002 and is offered via a SaaS (software as a service) business model by Text. Organizations use LiveChat as a single point of contact to manage customer service and online sales activities with a single program. == Product == LiveChat is proprietary software. LiveChat's website chat widget can be embedded on customers' websites as a small chat box, often displayed in the bottom right corner of the web browser. It can be used to conduct chats, share files and save transcripts. The agent application is used by company employees to respond to questions asked by the customers. This is available through both web-based application, desktop applications, and mobile apps. Web chat sessions can be initiated by the visiting customer, or by the agent, either manually or automatically by the LiveChat system when the visitor meets the predefined criteria (i.e. searched keyword, time on website, encountered error, etc.). LiveChat's system attempts to identify the best prospects visiting a website based on data gathered from past purchasing decisions. Other features include real-time website traffic monitoring, built-in ticketing system and agents' efficiency analytics. LiveChat is available in 48 languages. == Research and reception == Reviewing LiveChat's usefulness for online learning in 2020, psychologist Jaclyn Broadbent said "LiveChat occurs as a real-time conversation, it can be time-consuming for staff and disruptive to other tasks." However, using it has resulted in reduced communication traffic from other channels, such as the discussion boards or email. As a teacher, the best time to be available on LiveChat is when you are doing other administrative jobs." Since 2014 LiveChat has been publishing Customer Service Report - an annual study of customer satisfaction and analysis of online business communication trends. It includes research of thousands of companies and millions of customer service email and live support interactions.

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  • MarkLogic Server

    MarkLogic Server

    MarkLogic Server is a document-oriented database developed by MarkLogic. It is a NoSQL multi-model database that evolved from an XML database to natively store JSON documents and RDF triples, the data model for semantics. MarkLogic is designed to be a data hub for operational and analytical data. == History == MarkLogic Server was built to address shortcomings with existing search and data products. The product first focused on using XML as the document markup standard and XQuery as the query standard for accessing collections of documents up to hundreds of terabytes in size. Currently the MarkLogic platform is widely used in publishing, government, finance and other sectors. MarkLogic's customers are mostly Global 2000 companies. == Technology == MarkLogic uses documents without upfront schemas to maintain a flexible data model. In addition to having a flexible data model, MarkLogic uses a distributed, scale-out architecture that can handle hundreds of billions of documents and hundreds of terabytes of data. It has received Common Criteria certification, and has high availability and disaster recovery. MarkLogic is designed to run on-premises and within public or private cloud environments like Amazon Web Services. == Features == Indexing MarkLogic indexes the content and structure of documents including words, phrases, relationships, and values in over 200 languages with tokenization, collation, and stemming for core languages. Functionality includes the ability to toggle range indexes, geospatial indexes, the RDF triple index, and reverse indexes on or off based on your data, the kinds of queries that you will run, and your desired performance. Full-text search MarkLogic supports search across its data and metadata using a word or phrase and incorporates Boolean logic, stemming, wildcards, case sensitivity, punctuation sensitivity, diacritic sensitivity, and search term weighting. Data can be searched using JavaScript, XQuery, SPARQL, and SQL. Semantics MarkLogic uses RDF triples to provide semantics for ease of storing metadata and querying. ACID Unlike other NoSQL databases, MarkLogic maintains ACID consistency for transactions. Replication MarkLogic provides high availability with replica sets. Scalability MarkLogic scales horizontally using sharding. MarkLogic can run over multiple servers, balancing the load or replicating data to keep the system up and running in the event of hardware failure. Security MarkLogic has built in security features such as element-level permissions and data redaction. Optic API for Relational Operations An API that lets developers view their data as documents, graphs or rows. Security MarkLogic provides redaction, encryption, and element-level security (allowing for control on read and write rights on parts of a document). == Applications == Banking Big Data Fraud prevention Insurance Claims Management and Underwriting Master data management Recommendation engines == Licensing == MarkLogic is available under various licensing and delivery models, namely a free Developer or an Essential Enterprise license.[3] Licenses are available from MarkLogic or directly from cloud marketplaces such as Amazon Web Services and Microsoft Azure. == Releases == 2001 – Cerisent XQE 1: ACID transactions, Full-text search, XML Storage, XQuery, Role-based security 2004 – Cerisent XQE 2: Scale-out architecture, Enhanced search (stemming, thesaurus, wildcard), Backup and restore 2005 – MarkLogic Server 3: Continuing search improvements, Content Processing Framework (including PDF, Word, Excel, PPT), Failover 2008 – MarkLogic Server 4: Geospatial search, entity extraction, advanced XQuery, performance, scalability enhancements, auditing 2011 – MarkLogic Server 5: Flexible replication / DDIL, real-time indexing, advanced search, improved analytics, concurrency enhancements 2012 – MarkLogic Server 6: REST and Java APIs, App Builder, enhanced UI, improved search 2013 – MarkLogic Server 7: Semantic graph, bitemporal data, tiered storage, improved search, better management 2015 – MarkLogic Server 8: A Native JSON storage, Server-side JavaScript, Bitemporal, Node.js client API, Incremental backup, Flexible replication[16] 2017 – MarkLogic Server 9: Data integration across Relational and Non-Relational data, Advanced Encryption, Element Level Security, Redaction 2019 – MarkLogic Server 10: Enhanced Data Hub, improved SQL, security, analytics performance, cloud support 2022 – MarkLogic Server 11: MarkLogic Ops Director (Monitoring and Administration Improvements), expanded PKI 2025 – MarkLogic Server 12: Generative AI and Native Vector Search, Graph Algorithm Support, Virtual TDEs (relational views on the fly)

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  • SQL/PSM

    SQL/PSM

    SQL/PSM (SQL/Persistent Stored Modules) is an ISO standard mainly defining an extension of SQL with a procedural language for use in stored procedures. Initially published in 1996 as an extension of SQL-92 (ISO/IEC 9075-4:1996, a version sometimes called PSM-96 or even SQL-92/PSM), SQL/PSM was later incorporated into the multi-part SQL:1999 standard, and has been part 4 of that standard since then, most recently in SQL:2023. The SQL:1999 part 4 covered less than the original PSM-96 because the SQL statements for defining, managing, and invoking routines were actually incorporated into part 2 SQL/Foundation, leaving only the procedural language itself as SQL/PSM. The SQL/PSM facilities are still optional as far as the SQL standard is concerned; most of them are grouped in Features P001-P008. SQL/PSM standardizes syntax and semantics for control flow, exception handling (called "condition handling" in SQL/PSM), local variables, assignment of expressions to variables and parameters, and (procedural) use of cursors. It also defines an information schema (metadata) for stored procedures. SQL/PSM is one language in which methods for the SQL:1999 structured types can be defined. The other is Java, via SQL/JRT. SQL/PSM is derived, seemingly directly, from Oracle's PL/SQL. Oracle developed PL/SQL and released it in 1991, basing the language on the US Department of Defense's Ada programming language. However, Oracle has maintained a distance from the standard in its documentation. IBM's SQL PL (used in DB2) and Mimer SQL's PSM were the first two products officially implementing SQL/PSM. It is commonly thought that these two languages, and perhaps also MySQL/MariaDB's procedural language, are closest to the SQL/PSM standard. However, a PostgreSQL addon implements SQL/PSM (alongside its other procedural languages like the PL/SQL-derived plpgsql), although it is not part of the core product. RDF functionality in OpenLink Virtuoso was developed entirely through SQL/PSM, combined with custom datatypes (e.g., ANY for handling URI and Literal relation objects), sophisticated indexing, and flexible physical storage choices (column-wise or row-wise).

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