The ‘open web’ refers to the non-proprietary portion of the world wide web (WWW). That is, the portion that is free and freely accessible, as it was when it was launched. The opposite of the open web is a proprietary “walled-garden”, such as Facebook.
Category: Computing & data (Page 87 of 99)
Computing and data is a broad category. Our coverage of computing is largely limited to software, and we are mostly focused on unstructured data, semi-structured data, or mixed data that includes structured data.
Topics include computing platforms, analytics, data science, data modeling, database technologies, machine learning / AI, Internet of Things (IoT), blockchain, augmented reality, bots, programming languages, natural language processing applications such as machine translation, and knowledge graphs.
Related categories: Semantic technologies, Web technologies & information standards, and Internet and platforms.
‘Document computing’ was a term used to cover a collection of technologies that emerged as computer, or electronic publishing became a growing industry late 1980s and early 1990s. The idea was to differentiate the creation, management and delivery of unstructured data from the traditional and still prevalent structured data orientation of computing applications. It was one of the keynote topics at the first Documation conference in 1994 . Also see the more current, largely overlapping ‘content technology’.
Also see:
Gilbane Report Vol 6, Num 1 — Document Computing – Is This Our Business?
In the early days of information technology (1950s – 1970s), computers were mostly mainframes and the information mostly structured data managed by information systems based on hierarchical and then relational databases.
With the emergence of descriptive markup languages such as SGML, XML, and JSON that add structure other forms of unstructured data or content such as text and streaming data, as well as NoSQL and graph database, linked data, and knowledge graph technologies, the distinction between structured and unstructured data or content is less relevant. Modern data lakes store structured, semi-structured, and unstructured data.
“Content technology” is a form of information technology that uses computing technology to create, retrieve, process, manage, store, share, and distribute unstructured data, such as narrative text and audio visual media, and typically incorporates or integrates with systems that manage structured data . The term emerged as early web content management systems proliferated, but includes any technology that processes some form of unstructured data, such as authoring, publishing, natural language processing, search and retrieval.
The Gilbane Report on Open Information & Document Systems (ISSN 1067-8719) was periodical launched in March, 1993 by Publishing Technology Management Inc. which was founded by Frank Gilbane, its president, in June, 1987.
The Gilbane Report was sold to CAP Ventures Inc in December 1994, who published it until May, 1999, when it was bought by Bluebill Advisors, Inc. a consulting and advisory firm founded by Frank Gilbane. Bluebill Advisors continued to publish the Gilbane Report until March, 2005. The Gilbane Report issues from 1993 – 2005 remain available in either HTML or PDF (or both), on the Gilbane Advisor website, which is owned by Bluebill Advisors Inc.
Below is a link to the first issue of the Gilbane Report. There is also a PDF version.
Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one natural language to another.
In the 80s and 90s MT software was rule-based, but in the 2000s statistical analysis and the re-emergence of neural networking and more advanced machine learning techniques have proved to be far more successful.
Artificial General Intelligence (AGI) refers to a type of artificial intelligence that is at least as capable as human intelligence. As powerful as machine learning has become with neural networking and deep learning techniques, it does not approach AGI, and when, or even if, it will is controversial.
A NoSQL (originally referring to “non SQL”, “non relational” or “not only SQL”) database provides a mechanism for storage and retrieval of data which is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, and include document databases, and XML databases, which along with object-oriented databases (OODB), focus on managing unstructured data or semi-structured data. “NoSQL” became popular in the early twenty-first century, triggered by the needs of Web 2.0 companies such as Facebook, Google, and Amazon.com. NoSQL databases are increasingly used in big data and real-time web applications. NoSQL systems are also sometimes called “Not only SQL” to emphasize that they may support SQL-like query languages.
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