1.
Information system
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An information system is any organized system for the collection, organization, storage and communication of information. More specifically, it is the study of networks that people and organizations use to collect, filter, process. An information system is a group of components that interact to produce information, a computer information system is a system composed of people and computers that processes or interprets information. The term is sometimes used in more restricted senses to refer to only the software used to run a computerized database or to refer to only a computer system. An emphasis is placed on a system having a definitive boundary, users, processors, storage, inputs, outputs. Any specific information system aims to support operations, management and decision-making, an information system is the information and communication technology that an organization uses, and also the way in which people interact with this technology in support of business processes. Some authors make a distinction between information systems, computer systems, and business processes. Information systems typically include an ICT component but are not purely concerned with ICT, Information systems are also different from business processes. Information systems help to control the performance of business processes, alter argues for advantages of viewing an information system as a special type of work system. A work system is a system in humans or machines perform processes and activities using resources to produce specific products or services for customers. An information system is a system whose activities are devoted to capturing, transmitting, storing, retrieving, manipulating and displaying information. As such, information systems inter-relate with data systems on the one hand, an information system is a form of communication system in which data represent and are processed as a form of social memory. An information system can also be considered a language which supports human decision making. Information systems are the focus of study for organizational informatics. Silver et al. provided two views on IS that includes software, hardware, data, people, and procedures, zheng provided another system view of information system which also adds processes and essential system elements like environment, boundary, purpose, and interactions. Information technologies are a important and malleable resource available to executives. The CTO may also serve as CIO, and vice versa, the chief information security officer focuses on information security management. The six components that must come together in order to produce a system are, Hardware
2.
Full-text search
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In text retrieval, full-text search refers to techniques for searching a single computer-stored document or a collection in a full text database. Full-text search is distinguished from searches based on metadata or on parts of the texts represented in databases. In a full-text search, a search engine examines all of the words in every stored document as it tries to match search criteria, full-text-searching techniques became common in online bibliographic databases in the 1990s. Many websites and application programs provide full-text-search capabilities, some web search engines, such as AltaVista, employ full-text-search techniques, while others index only a portion of the web pages examined by their indexing systems. When dealing with a number of documents, it is possible for the full-text-search engine to directly scan the contents of the documents with each query. This is what some tools, such as grep, do when searching, the indexing stage will scan the text of all the documents and build a list of search terms. In the search stage, when performing a specific query, only the index is referenced, the indexer will make an entry in the index for each term or word found in a document, and possibly note its relative position within the document. Usually the indexer will ignore stop words that are common and insufficiently meaningful to be useful in searching. Some indexers also employ language-specific stemming on the words being indexed, for example, the words drives, drove, and driven will be recorded in the index under the single concept word drive. Recall measures the quantity of relevant results returned by a search, recall is the ratio of relevant results returned to all relevant results. Precision is the number of relevant results returned to the number of results returned. The diagram at right represents a low-precision, low-recall search, in the diagram the red and green dots represent the total population of potential search results for a given search. Red dots represent irrelevant results, and green dots represent relevant results, relevancy is indicated by the proximity of search results to the center of the inner circle. Of all possible results shown, those that were returned by the search are shown on a light-blue background. In the example only 1 relevant result of 3 possible relevant results was returned, so the recall is a low ratio of 1/3. The precision for the example is a very low 1/4, or 25%, due to the ambiguities of natural language, full-text-search systems typically includes options like stop words to increase precision and stemming to increase recall. Controlled-vocabulary searching also helps alleviate low-precision issues by tagging documents in such a way that ambiguities are eliminated, the trade-off between precision and recall is simple, an increase in precision can lower overall recall, while an increase in recall lowers precision. Full text searching is likely to many documents that are not relevant to the intended search question
3.
Information overload
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Information overload is a term used to describe the difficulty of understanding an issue and effectively making decisions when one has too much information about that issue. The term is popularized by Alvin Toffler in his bestselling 1970 book Future Shock, speier et al. stated, Information overload occurs when the amount of input to a system exceeds its processing capacity. Decision makers have fairly limited cognitive processing capacity, consequently, when information overload occurs, it is likely that a reduction in decision quality will occur. In recent years, the information overload has evolved into phrases such as information glut. What was once a term grounded in cognitive psychology has evolved into a metaphor used outside the world of academia. The social psychologist Stanley Milgram later used the concept of information overload to explain bystander behavior, psychologists have recognized for many years that humans have a limited capacity to store current information in the memory. Psychologist George Armitage Miller was very influential in regard, proposing that people can process about seven chunks of information at a time. Miller says that under conditions, people become confused and are likely to make poorer decisions based on the information they have received as opposed to making informed ones. It will be almost as convenient to search for some bit of truth concealed in nature as it will be to find it hidden away in a multitude of bound volumes. Information overload has been documented throughout periods where advances in technology have increased a production of information, as early as the 3rd or 4th century BC, people regarded information overload with disapproval. Similar complaints around the growth of books were also mentioned in China, around 1440 AD, Johannes Gutenberg invented the printing press and this marked another period of information proliferation. As a result of lowering costs, generation of printed materials ranging from pamphlets, manuscripts to books were made available to the average person. Many grew concerned with the rise of books in Europe, especially in England, France, from 1750 to 1800, there was a 150% increase in the production of books. In 1702, jurist and philosopher Christian Thomasius expressed concerns about the overproduction of books, Thomasius felt with more books being published, the standards of publishing a book decreased. In the second half of the 20th Century, advances in computer, Social media has resulted in social information overload, which can occur on sites like Facebook, and technology is changing to serve our social culture. In todays society, day-to-day activities increasingly involve the world where information technology exacerbates the number of interruptions that occur in the work environment. A2012 survey by McKinsey Global Institute found that the average worker spends 28% of work time managing email, adding this decades use of the Internet, management may be even more disrupted in their decision making, and may result in more poor decisions. Thus, the PIECES framework mentions information overload as a problem in existing information systems
4.
Web search engine
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A web search engine is a software system that is designed to search for information on the World Wide Web. The search results are presented in a line of results often referred to as search engine results pages. The information may be a mix of web pages, images, some search engines also mine data available in databases or open directories. Unlike web directories, which are maintained only by human editors, Internet search engines themselves predate the debut of the Web in December 1990. The Who is user search dates back to 1982 and the Knowbot Information Service multi-network user search was first implemented in 1989, the first well documented search engine that searched content files, namely FTP files was Archie, which debuted on 10 September 1990. Prior to September 1993 the World Wide Web was entirely indexed by hand, there was a list of webservers edited by Tim Berners-Lee and hosted on the CERN webserver. One historical snapshot of the list in 1992 remains, but as more and more web servers went online the central list could no longer keep up, on the NCSA site, new servers were announced under the title Whats New. The first tool used for searching content on the Internet was Archie, the name stands for archive without the v. It was created by Alan Emtage, Bill Heelan and J. Peter Deutsch, the rise of Gopher led to two new search programs, Veronica and Jughead. Like Archie, they searched the file names and titles stored in Gopher index systems, Veronica provided a keyword search of most Gopher menu titles in the entire Gopher listings. Jughead was a tool for obtaining information from specific Gopher servers. While the name of the search engine Archie Search Engine was not a reference to the Archie comic book series, Veronica and Jughead are characters in the series, thus referencing their predecessor. In the summer of 1993, no search engine existed for the web, oscar Nierstrasz at the University of Geneva wrote a series of Perl scripts that periodically mirrored these pages and rewrote them into a standard format. This formed the basis for W3Catalog, the webs first primitive search engine, in June 1993, Matthew Gray, then at MIT, produced what was probably the first web robot, the Perl-based World Wide Web Wanderer, and used it to generate an index called Wandex. The purpose of the Wanderer was to measure the size of the World Wide Web, the webs second search engine Aliweb appeared in November 1993. Aliweb did not use a web robot, but instead depended on being notified by website administrators of the existence at each site of a file in a particular format. NCSAs Mosaic™ - Mosaic wasnt the first Web browser, but it was the first to make a major splash. In November 1993, Mosaic v 1, jumpStation used a web robot to find web pages and to build its index, and used a web form as the interface to its query program
5.
Database
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A database is a well organized collection of data. It is the collection of schemas, tables, queries, reports, views, a database management system is a computer software application that interacts with the user, other applications, and the database itself to capture and analyze data. A general-purpose DBMS is designed to allow the definition, creation, querying, update, well-known DBMSs include MySQL, PostgreSQL, MongoDB, MariaDB, Microsoft SQL Server, Oracle, Sybase, SAP HANA, MemSQL and IBM DB2. Sometimes a DBMS is loosely referred to as a database, formally, a database refers to a set of related data and the way it is organized. The DBMS provides various functions that allow entry, storage and retrieval of large quantities of information, because of the close relationship between them, the term database is often used casually to refer to both a database and the DBMS used to manipulate it. Outside the world of information technology, the term database is often used to refer to any collection of related data. This article is concerned only with databases where the size and usage requirements necessitate use of a management system. Update – Insertion, modification, and deletion of the actual data, retrieval – Providing information in a form directly usable or for further processing by other applications. The retrieved data may be available in a form basically the same as it is stored in the database or in a new form obtained by altering or combining existing data from the database. Both a database and its DBMS conform to the principles of a database model. Database system refers collectively to the model, database management system. Physically, database servers are dedicated computers that hold the actual databases and run only the DBMS, Database servers are usually multiprocessor computers, with generous memory and RAID disk arrays used for stable storage. RAID is used for recovery of data if any of the disks fail, hardware database accelerators, connected to one or more servers via a high-speed channel, are also used in large volume transaction processing environments. DBMSs are found at the heart of most database applications, DBMSs may be built around a custom multitasking kernel with built-in networking support, but modern DBMSs typically rely on a standard operating system to provide these functions. Since DBMSs comprise a significant market, computer and storage vendors often take into account DBMS requirements in their own development plans, databases are used to support internal operations of organizations and to underpin online interactions with customers and suppliers. Databases are used to hold information and more specialized data. A DBMS has evolved into a software system and its development typically requires thousands of human years of development effort. Some general-purpose DBMSs such as Adabas, Oracle and DB2 have been undergoing upgrades since the 1970s, general-purpose DBMSs aim to meet the needs of as many applications as possible, which adds to the complexity
6.
Mind map
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A mind map is a diagram used to visually organize information. A mind map is hierarchical and shows relationships among pieces of the whole. It is often created around a concept, drawn as an image in the center of a blank page, to which associated representations of ideas such as images, words. Major ideas are connected directly to the concept, and other ideas branch out from those. Mind maps can be drawn by hand, either as rough notes during a lecture, meeting or planning session, for example, Mind maps are considered to be a type of spider diagram. A similar concept in the 1970s was idea sun bursting, some of the earliest examples of such graphical records were developed by Porphyry of Tyros, a noted thinker of the 3rd century, as he graphically visualized the concept categories of Aristotle. Philosopher Ramon Llull also used such techniques, the semantic network was developed in the late 1950s as a theory to understand human learning and developed further by Allan M. Collins and M. Ross Quillian during the early 1960s. Mind maps are similar in structure to concept maps, developed by learning experts in the 1970s. Buzans specific approach, and the introduction of the mind map arose during a 1974 BBC TV series he hosted, called Use Your Head. In this show, and companion series, Buzan promoted his conception of radial tree, diagramming key words in a colorful, radiant. Buzan says the idea was inspired by Alfred Korzybskis general semantics as popularized in science fiction novels, such as those of Robert A. Heinlein and A. E. van Vogt. He argues that while traditional outlines force readers to scan left to right and top to bottom, Buzan suggests the following guidelines for creating mind maps, Start in the center with an image of the topic, using at least 3 colors. Use images, symbols, codes, and dimensions throughout your mind map, select key words and print using upper or lower case letters. Each word/image is best alone and sitting on its own line, the lines should be connected, starting from the central image. The lines become thinner as they radiate out from the center, make the lines the same length as the word/image they support. Use multiple colors throughout the map, for visual stimulation. Develop your own style of mind mapping. Use emphasis and show associations in your mind map, keep the mind map clear by using radial hierarchy or outlines to embrace your branches
7.
As We May Think
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As We May Think is a 1945 essay by Vannevar Bush which has been described as visionary and influential, anticipating many aspects of information society. It was first published in The Atlantic in July 1945 and republished in a version in September 1945—before and after the atomic bombings of Hiroshima. Through this machine, Bush hoped to transform an information explosion into a knowledge explosion, the article was a reworked and expanded version of Bushs essay Mechanization and the Record. Here, he described a machine that would combine lower level technologies to achieve a level of organized knowledge. Shortly after the publication of this essay, Bush coined the term memex in a letter written to the editor of Fortune magazine and that letter became the body of As We May Think, which added only an introduction and conclusion. The memex, in essence, reflects a library of collective knowledge stored in a piece of machinery described in his essay as a piece of furniture. The Atlantic publication of Bushs article was followed, in the September 10,1945 issue of Life magazine, by a reprint that showed illustrations of the proposed memex desk and automatic typewriter. Bush also discussed other technologies such as dry photography and microphotography where he elaborates on the potentialities of their future use, Bush envisioned the ability to retrieve several articles or pictures on one screen, with the possibility of writing comments that could be stored and recalled together. He believed people would create links between related articles, thus mapping the thought process and path of each user and saving it for others to experience, Bushs article also laid the foundation for new media. Today, storage has greatly surpassed the level imagined by Vannevar Bush, a library of a million volumes could be compressed into one end of a desk. It can be in one place, unless duplicates are used. This description resembles popular file systems of modern operating systems. Bush urges that scientists should turn to the task of creating more efficient accessibility to our fluctuating store of knowledge. For years inventions have extended peoples physical powers rather than the powers of their mind and he argues that the instruments are at hand which, if properly developed, will give society access to and command over the inherited knowledge of the ages. The perfection of these instruments, he suggests, should be the first objective of our scientists. Through this process society would be able to focus and evolve past the existing knowledge rather than looping through infinite calculations and we should be able to pass the tedious work of numbers to machines and work on the intricate theory which puts them best of use. To exemplify the importance of this concept, consider the process involved in simple shopping, Every time a charge sale is made, there are a number of things to be done. The inventory needs to be revised, the needs to be given credit for the sale, the general accounts need an entry, and most important
8.
Vannevar Bush
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He is also known in engineering for his work on analog computers, for founding Raytheon, and for the memex, a hypothetical adjustable microfilm viewer with a structure analogous to that of hypertext. The memex influenced generations of scientists, who drew inspiration from its vision of the future. He was chiefly responsible for the movement that led to the creation of the National Science Foundation, for his masters thesis, Bush invented and patented a profile tracer, a mapping device for assisting surveyors. It was the first of a string of inventions and he joined the Department of Electrical Engineering at Massachusetts Institute of Technology in 1919, and founded the company now known as Raytheon in 1922. Starting in 1927, Bush constructed a differential analyzer, a computer with some digital components that could solve differential equations with as many as 18 independent variables. An offshoot of the work at MIT by Bush and others was the beginning of digital circuit design theory, Bush became vice president of MIT and dean of the MIT School of Engineering in 1932, and president of the Carnegie Institution of Washington in 1938. Bush was appointed to the National Advisory Committee for Aeronautics in 1938, Bush was a well-known policymaker and public intellectual during World War II, when he was in effect the first presidential science advisor. As head of NDRC and OSRD, he initiated the Manhattan Project, Vannevar Bush was born in Everett, Massachusetts, on March 11,1890, the third child and only son of Perry Bush, the local Universalist pastor, and his wife Emma Linwood née Paine. He had two sisters, Edith and Reba. He was named after John Vannevar, an old friend of the family who had attended Tufts College with Perry, the family moved to Chelsea, Massachusetts, in 1892, and Bush graduated from Chelsea High School in 1909. He then attended Tufts, like his father before him, a popular student, he was vice president of his sophomore class, and president of his junior class. During his senior year, he managed the football team and he became a member of the Alpha Tau Omega fraternity, and dated Phoebe Clara Davis, who also came from Chelsea. Tufts allowed students to gain a degree in four years simultaneously with a bachelors degree, so for his masters thesis. This was a device for assisting surveyors that looked like a lawn mower and it had two bicycle wheels, and a pen that plotted the terrain over which it traveled. It was the first of a string of inventions, on graduation in 1913 he received both bachelor of science and master of science degrees. After graduation, Bush worked at General Electric in Schenectady, New York, as a test man, his job was to assess equipment to ensure that it was safe. He transferred to GEs plant in Pittsfield, Massachusetts, to work on high voltage transformers, but after a fire out at the plant, Bush. He returned to Tufts in October 1914 to teach mathematics, and he was awarded a $1,500 scholarship to study at Clark University as a doctoral student of Arthur Gordon Webster, but Webster wanted Bush to study acoustics
9.
Emanuel Goldberg
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Emanuel Goldberg was an Israeli physicist and inventor. He was born in Moscow and moved first to Germany and later to Israel. He described himself as “a chemist by learning, physicist by calling, and a mechanic by birth. ”He contributed a wide range of theoretic and practical advances relating to light and media and was the head of Zeiss Ikon. His inventions include microdots, the Kinamo movie camera, the Contax 35 mm camera, an early search engine. Goldberg was born in Moscow on 31 August 1881 the son of Grigorii Ignat’evich Goldberg, earlier interested in engineering, he studied Chemistry at the University of Moscow and at several German universities, and remained in Germany after 1904 to avoid antisemitism in Russia. In 1906 he received a Ph. D from the University of Leipzig for research at the Institute for Physical Chemistry, led by Wilhelm Ostwald on the kinetics of photochemical reactions. In 1917 Goldberg was recruited by the Carl Zeiss Stiftung to become a director of its photographic products subsidiary Ica in Dresden where he introduced the spring-driven Kinamo movie camera. In 1926 a “Fusion” of four leading photographic firms formed Zeiss Ikon under Goldberg’s leadership until he was kidnapped by Nazis in 1933, a photograph taken 1943 by John Phillips for Life Magazine shows Goldberg in his workshop in Palestine. He retired in 1960 but continued his research and died in Tel Aviv on 13 September 1970, Goldberg made films of himself and his family as promotional shorts and, in 1927, a skiing drama, “Ein Sprung. The Kinamo was used by Joris Ivens and other avant-garde and documentary filmmakers in the late 1920s, in 1925 Goldberg demonstrated and published a technique for making microdot at a resolution equivalent to the text 50 complete Bibles per square inch. In 1937, Goldberg presented a paper at the World Congress of Universal Documentation on an early copying camera he had invented. At Ica and Zeiss Ikon Goldberg was involved in many innovations and led the design of famous Contax 35 mm still camera. ”In Germany Goldberg was noted for his displays at exhibitions, served as consultant on aerial photography in the First World War. In Palestine and later Israel he was engaged as an advisory to both civilian and military spheres. The apprenticeship scheme that he introduced in Tel Aviv provided advanced technical skills to many who went on to develop the Israeli high tech industry, in 1968, Goldberg was awarded the Israel Prize, in exact science. On 28 June 1907 Goldberg married Sophie Posniak and they had a son, Herbert Goldberg and a daughter Renate Eva, who later changed her name to Chava. Chava married Mordechai Gichon in 1948, Goldberg, Emanuel, Beiträge zur Kinetik photochemischer Reaktionen. Goldberg, Emanuel, Densograph, ein Registrierapparat zur Messung der Schwärzung von photographischen Platten, in, Eder, Josef Maria, Jahrbuch für Photographie und Reproduktionstechnik. Goldberg, Emanuel, A new process of micro-photography, Goldberg, Emanuel, Die Herstellung neutral grauer Keile und verlaufender Filter für Photometrie und Photographie
10.
UNIVAC
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UNIVAC is the name of a line of electronic digital stored-program computers starting with the products of the Eckert-Mauchly Computer Corporation. Later the name was applied to a division of the Remington Rand company, UNIVAC is an acronym for UNIVersal Automatic Computer. The BINAC, built by the Eckert-Mauchly Computer Corporation, was the first general-purpose computer for commercial use, the descendants of the later UNIVAC1107 continue today as products of the Unisys company. J. Presper Eckert and John Mauchly built the ENIAC at the University of Pennsylvanias Moore School of Electrical Engineering between 1943 and 1946 and that company first built a computer called BINAC for Northrop Aviation. Afterwards began the development of UNIVAC, UNIVAC was first intended for the Bureau of the Census, which paid for much of the development, and then was put in production. With the death of EMCCs chairman and chief financial backer Harry L. Straus in a crash on October 25,1949. Eckert and Mauchly now reported to Leslie Groves, the army general who had managed the Manhattan Project. The most famous UNIVAC product was the UNIVAC I mainframe computer of 1951 and this incident is particularly noteworthy because the computer predicted an Eisenhower landslide when traditional pollsters all called it for Adlai Stevenson. The numbers were so skewed that CBSs news boss in New York, Mickelson, decided the computer was in error, instead they showed some staged theatrics that suggested the computer was not responsive, and announced it was predicting 8-7 odds for an Eisenhower win. Remington Rand had its own calculating machine lab in Norwalk, Connecticut and this severely annoyed those who had been with ERA and with the Norwalk laboratory. In 1955 Remington Rand merged with Sperry Corporation to become Sperry Rand, the UNIVAC division of Remington Rand was renamed the Univac division of Sperry Rand. General Douglas MacArthur was chosen to head the company, in the 1970s, after GE sold its computer business to Honeywell and RCA sold its to Univac, the analogy to the seven dwarfs became less apt and the remaining small firms became known as the BUNCH. To assist corporate identity the name was changed to Sperry Univac, along with Sperry Remington, Sperry New Holland, in 1978 Sperry Rand, an old fashioned conglomerate of disharmonious divisions, decided to concentrate on its computing interests and unrelated divisions were sold. The company dropped the Rand from its title and reverted to Sperry Corporation, in 1986, Sperry Corporation merged with Burroughs Corporation to become Unisys. Unisys continues to design and manufacture enterprise class computers with the ClearPath, in the course of its history, UNIVAC produced a number of separate model ranges. Early UNIVAC1100 series models were vacuum tube computers, the original model range was the UNIVAC I, the second commercial computer made in the United States. The main memory consisted of tanks of liquid mercury implementing delay line memory, the first machine was delivered on 31 March 1951. The UNIVAC II was an improvement to the UNIVAC I that UNIVAC first delivered in 1958 and it was fully compatible with existing UNIVAC I programs for both code and data
11.
Desk Set
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Desk Set is a 1957 American romantic comedy film directed by Walter Lang and starring Spencer Tracy and Katharine Hepburn. The screenplay was written by Phoebe Ephron and Henry Ephron from the 1955 play, The Desk Set, Watson has been involved for seven years with rising network executive Mike Cutler, with no marriage in sight. The network is negotiating a merger with another company, but is keeping it secret, to help the employees cope with the extra work that will result, the network head has ordered two computers, or electronic brains. Methods Engineer and efficiency expert Richard Sumner, the inventor of EMERAC, is brought in to see how the library functions, though extremely bright, as he gets to know Bunny Watson, he is surprised to discover that she is every bit his match. When they find out the computers are coming, the jump to the conclusion they are being replaced. Their fears seem to be confirmed when everyone on the staff receives a slip printed out by the new payroll computer. Fortunately, it out to be a mistake, the machine fired everybody in the company. Richard Sumner reveals his romantic interest in Bunny Watson, but she believes that EMERAC would always be his first priority, Sumner denies it, but then Watson puts him to the test, setting the machine to self-destruct. Sumner resists the urge to fix it as long as possible, in the play, Watson had only brief interactions with Sumner, and somewhat hostile. Screenwriters Phoebe and Henry Ephron built up the role of the efficiency expert, the exterior shots of the Federal Broadcasting Network seen in the film is actually the RCA Building at 30 Rockefeller Center in Rockefeller Center, the headquarters of NBC. The character of Bunny Watson was based on Agnes E. Law, following Desk Set their last film together would be 1967s Guess Whos Coming to Dinner. The computer referred to as EMERAC is a metonym for ENIAC. The researchers furnish incorrect information about the career of baseball player Ty Cobb, today the film is seen far more favorably, with the sharpness of the script praised in particular, it currently has a rare 100% rating on Rotten Tomatoes based on 17 reviews. A Canadian radio program, Bunny Watson, was named for, the film is recognized by American Film Institute in these lists,2002, AFIs 100 Years.100 Passions – Nominated List of American films of 1957 Desk Set at the Internet Movie Database
12.
National Institute of Standards and Technology
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The National Institute of Standards and Technology is a measurement standards laboratory, and a non-regulatory agency of the United States Department of Commerce. Its mission is to promote innovation and industrial competitiveness, in 1821, John Quincy Adams had declared Weights and measures may be ranked among the necessities of life to every individual of human society. From 1830 until 1901, the role of overseeing weights and measures was carried out by the Office of Standard Weights and Measures, president Theodore Roosevelt appointed Samuel W. Stratton as the first director. The budget for the first year of operation was $40,000, a laboratory site was constructed in Washington, DC, and instruments were acquired from the national physical laboratories of Europe. In addition to weights and measures, the Bureau developed instruments for electrical units, in 1905 a meeting was called that would be the first National Conference on Weights and Measures. Quality standards were developed for products including some types of clothing, automobile brake systems and headlamps, antifreeze, during World War I, the Bureau worked on multiple problems related to war production, even operating its own facility to produce optical glass when European supplies were cut off. Between the wars, Harry Diamond of the Bureau developed a blind approach radio aircraft landing system, in 1948, financed by the Air Force, the Bureau began design and construction of SEAC, the Standards Eastern Automatic Computer. The computer went into operation in May 1950 using a combination of vacuum tubes, about the same time the Standards Western Automatic Computer, was built at the Los Angeles office of the NBS and used for research there. A mobile version, DYSEAC, was built for the Signal Corps in 1954, due to a changing mission, the National Bureau of Standards became the National Institute of Standards and Technology in 1988. Following 9/11, NIST conducted the investigation into the collapse of the World Trade Center buildings. NIST had a budget for fiscal year 2007 of about $843.3 million. NISTs 2009 budget was $992 million, and it also received $610 million as part of the American Recovery, NIST employs about 2,900 scientists, engineers, technicians, and support and administrative personnel. About 1,800 NIST associates complement the staff, in addition, NIST partners with 1,400 manufacturing specialists and staff at nearly 350 affiliated centers around the country. NIST publishes the Handbook 44 that provides the Specifications, tolerances, the Congress of 1866 made use of the metric system in commerce a legally protected activity through the passage of Metric Act of 1866. NIST is headquartered in Gaithersburg, Maryland, and operates a facility in Boulder, nISTs activities are organized into laboratory programs and extramural programs. Effective October 1,2010, NIST was realigned by reducing the number of NIST laboratory units from ten to six, nISTs Boulder laboratories are best known for NIST‑F1, which houses an atomic clock. NIST‑F1 serves as the source of the official time. NIST also operates a neutron science user facility, the NIST Center for Neutron Research, the NCNR provides scientists access to a variety of neutron scattering instruments, which they use in many research fields
13.
Set (mathematics)
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In mathematics, a set is a well-defined collection of distinct objects, considered as an object in its own right. For example, the numbers 2,4, and 6 are distinct objects when considered separately, Sets are one of the most fundamental concepts in mathematics. Developed at the end of the 19th century, set theory is now a part of mathematics. In mathematics education, elementary topics such as Venn diagrams are taught at a young age, the German word Menge, rendered as set in English, was coined by Bernard Bolzano in his work The Paradoxes of the Infinite. A set is a collection of distinct objects. The objects that make up a set can be anything, numbers, people, letters of the alphabet, other sets, Sets are conventionally denoted with capital letters. Sets A and B are equal if and only if they have precisely the same elements. Cantors definition turned out to be inadequate, instead, the notion of a set is taken as a notion in axiomatic set theory. There are two ways of describing, or specifying the members of, a set, one way is by intensional definition, using a rule or semantic description, A is the set whose members are the first four positive integers. B is the set of colors of the French flag, the second way is by extension – that is, listing each member of the set. An extensional definition is denoted by enclosing the list of members in curly brackets, one often has the choice of specifying a set either intensionally or extensionally. In the examples above, for instance, A = C and B = D, there are two important points to note about sets. First, in a definition, a set member can be listed two or more times, for example. However, per extensionality, two definitions of sets which differ only in one of the definitions lists set members multiple times, define, in fact. Hence, the set is identical to the set. The second important point is that the order in which the elements of a set are listed is irrelevant and we can illustrate these two important points with an example, = =. For sets with many elements, the enumeration of members can be abbreviated, for instance, the set of the first thousand positive integers may be specified extensionally as, where the ellipsis indicates that the list continues in the obvious way. Ellipses may also be used where sets have infinitely many members, thus the set of positive even numbers can be written as
14.
Extended Boolean model
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The Extended Boolean model was described in a Communications of the ACM article appearing in 1983, by Gerard Salton, Edward A. Fox, and Harry Wu. The goal of the Extended Boolean model is to overcome the drawbacks of the Boolean model that has used in information retrieval. The Boolean model doesnt consider term weights in queries, and the set of a Boolean query is often either too small or too big. The idea of the model is to make use of partial matching. It combines the characteristics of the Vector Space Model with the properties of Boolean algebra and this way a document may be somewhat relevant if it matches some of the queried terms and will be returned as a result, whereas in the Standard Boolean model it wasnt. Thus, the extended Boolean model can be considered as a generalization of both the Boolean and vector space models, those two are special cases if suitable settings and definitions are employed, further, research has shown effectiveness improves relative to that for Boolean query processing. Other research has shown that relevance feedback and query expansion can be integrated with extended Boolean query processing, in the Extended Boolean model, a document is represented as a vector. Each i dimension corresponds to a term associated with the document. The weight vector associated with document dj can be represented as and this can be done using P-norms which extends the notion of distance to include p-distances, where 1 ≤ p ≤ ∞ is a new parameter. + p t p Consider the query q = ∨ K3, using P-norms they obtained an average precision improvement of 79%, 106% and 210% over the Standard model, for the CISI, CACM and INSPEC collections, respectively. The Standard Boolean model is still the most efficient, adaptive Feedback Methods in an Extended Boolean Model by Dr. Jongpill Choi Interpolation of the extended Boolean retrieval model Fox, E. Betrabet, S. Koushik, M. Lee, W. Information Retrieval, Algorithms and Data structures, Extended Boolean model, Prentice-Hall, skorkovská, Lucie, Ircing, Pavel, Experiments with Automatic Query Formulation in the Extended Boolean Model, Springer Berlin / Heidelberg Information retrieval
15.
Vector space model
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Vector space model or term vector model is an algebraic model for representing text documents as vectors of identifiers, such as, for example, index terms. It is used in filtering, information retrieval, indexing. Its first use was in the SMART Information Retrieval System, documents and queries are represented as vectors. D j = q = Each dimension corresponds to a separate term, if a term occurs in the document, its value in the vector is non-zero. Several different ways of computing these values, also known as weights, have been developed, one of the best known schemes is tf-idf weighting. The definition of term depends on the application, typically terms are single words, keywords, or longer phrases. If words are chosen to be the terms, the dimensionality of the vector is the number of words in the vocabulary, Vector operations can be used to compare documents with queries. See cosine similarity for further information, in the classic vector space model proposed by Salton, Wong and Yang the term-specific weights in the document vectors are products of local and global parameters. The model is known as term frequency-inverse document frequency model, | D | is the total number of documents in the document set, | | is the number of documents containing the term t. When using Boolean weights, any document lies in a vertex in a n-dimensional hypercube, therefore, the possible document representations are 2 n and the maximum Euclidean distance between pairs is n. As documents are added to the document collection, the region defined by the hypercubes vertexes become more populated, unlike Boolean, when a document is added using tf-idf weights, the idfs of the terms in the new document decrease while that of the remaining terms increase. In average, as documents are added, the region where documents lie expands regulating the density of the entire collection representation and this behavior models the original motivation of Salton and his colleagues that a document collection represented in a low density region could yield better retrieval results. The order in which the appear in the document is lost in the vector space representation. Theoretically assumed terms are statistically independent, weighting is intuitive but not very formal. Many of these difficulties can, however, be overcome by the integration of tools, including mathematical techniques such as singular value decomposition. Apache Lucene is a high-performance, full-featured text search library written entirely in Java. Gensim is a Python+NumPy framework for Vector Space modelling and it contains incremental algorithms for Tf–idf, Latent Semantic Indexing, Random Projections and Latent Dirichlet Allocation. Weka is a data mining package for Java including WordVectors
16.
Bayes' theorem
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In probability theory and statistics, Bayes’ theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. One of the applications of Bayes’ theorem is Bayesian inference. When applied, the involved in Bayes’ theorem may have different probability interpretations. With the Bayesian probability interpretation the theorem expresses how a subjective degree of belief should rationally change to account for availability of related evidence, Bayesian inference is fundamental to Bayesian statistics. Bayes’ theorem is named after Rev. Thomas Bayes, who first provided an equation that allows new evidence to update beliefs. It was further developed by Pierre-Simon Laplace, who first published the modern formulation in his 1812 “Théorie analytique des probabilités. ”Sir Harold Jeffreys put Bayes’ algorithm and Laplaces formulation on an axiomatic basis. Jeffreys wrote that Bayes’ theorem “is to the theory of probability what the Pythagorean theorem is to geometry. ”Bayes theorem is stated mathematically as the equation, P = P P P. P and P are the probabilities of observing A and B without regard to each other, P, a conditional probability, is the probability of observing event A given that B is true. P is the probability of observing event B given that A is true, Bayes’ theorem was named after the Reverend Thomas Bayes, who studied how to compute a distribution for the probability parameter of a binomial distribution. Bayes’ unpublished manuscript was edited by Richard Price before it was posthumously read at the Royal Society. Price edited Bayes’ major work “An Essay towards solving a Problem in the Doctrine of Chances”, Price wrote an introduction to the paper which provides some of the philosophical basis of Bayesian statistics. In 1765 he was elected a Fellow of the Royal Society in recognition of his work on the legacy of Bayes, the French mathematician Pierre-Simon Laplace reproduced and extended Bayes’ results in 1774, apparently quite unaware of Bayes’ work. The Bayesian interpretation of probability was developed mainly by Laplace, stephen Stigler suggested in 1983 that Bayes’ theorem was discovered by Nicholas Saunderson, a blind English mathematician, some time before Bayes, that interpretation, however, has been disputed. Martyn Hooper and Sharon McGrayne have argued that Richard Prices contribution was substantial, By modern standards, Price discovered Bayes’ work, recognized its importance, corrected it, contributed to the article, and found a use for it. The modern convention of employing Bayes’ name alone is unfair but so entrenched that anything else makes little sense, suppose a drug test is 99% sensitive and 99% specific. That is, the test will produce 99% true positive results for drug users, suppose that 0. 5% of people are users of the drug. If a randomly selected individual tests positive, what is the probability that he is a user and this surprising result arises because the number of non-users is very large compared to the number of users, thus the number of false positives outweighs the number of true positives. To use concrete numbers, if 1000 individuals are tested, there are expected to be 995 non-users and 5 users, from the 995 non-users,0.01 ×995 ≃10 false positives are expected
17.
Latent Dirichlet allocation
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LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael I. Jordan in 2003. Essentially the same model was proposed independently by J. K. Pritchard, M. Stephens. Both papers have been influential, with 16488 and 18170 citations respectively by December 2016. In LDA, each document may be viewed as a mixture of topics where each document is considered to have a set of topics that are assigned to it via LDA. This is identical to probabilistic latent semantic analysis, except that in LDA the topic distribution is assumed to have a sparse Dirichlet prior. The sparse Dirichlet priors encode the intuition that documents cover only a set of topics. In practice, this results in a better disambiguation of words, LDA is a generalisation of the pLSA model, which is equivalent to LDA under a uniform Dirichlet prior distribution. For example, an LDA model might have topics that can be classified as CAT_related and DOG_related, a topic has probabilities of generating various words, such as milk, meow, and kitten, which can be classified and interpreted by the viewer as CAT_related. Naturally, the cat itself will have high probability given this topic. The DOG_related topic likewise has probabilities of generating each word, puppy, bark, words without special relevance, such as the, will have roughly even probability between classes. A topic is not strongly defined, neither semantically nor epistemologically and it is identified on the basis of supervised labeling and pruning on the basis of their likelihood of co-occurrence. A lexical word may occur in several topics with a different probability, however, each document is assumed to be characterized by a particular set of topics. This is akin to the bag of words model assumption. With plate notation, the dependencies among the variables can be captured concisely. The boxes are “plates” representing replicates, the outer plate represents documents, while the inner plate represents the repeated choice of topics and words within a document. M denotes the number of documents, N the number of words in a document, the words w i j are the only observable variables, and the other variables are latent variables. As proposed in the paper, a sparse Dirichlet prior can be put over the topic-word distribution. This codes the intuition that the probability of topics is focussed on a set of words
18.
Learning to rank
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Training data consists of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a judgment for each item. The ranking models purpose is to rank, i. e. produce a permutation of items in new, ranking is a central part of many information retrieval problems, such as document retrieval, collaborative filtering, sentiment analysis, and online advertising. A possible architecture of a search engine is shown in the figure to the right. Training data consists of queries and documents matching them together with relevance degree of each match and it may be prepared manually by human assessors, who check results for some queries and determine relevance of each result. It is not feasible to check relevance of all documents, and so typically a technique called pooling is used — only the top few documents, alternatively, training data may be derived automatically by analyzing clickthrough logs, query chains, or such search engines features as Googles SearchWiki. Training data is used by an algorithm to produce a ranking model which computes relevance of documents for actual queries. Typically, users expect a search query to complete in a time, which makes it impossible to evaluate a complex ranking model on each document in the corpus. This phase is called top- k document retrieval and many heuristics were proposed in the literature to accelerate it, such as using a documents static quality score, in the second phase, a more accurate but computationally expensive machine-learned model is used to re-rank these documents. In Recommender systems for identifying a ranked list of related articles to recommend to a user after he or she has read a current news article. For convenience of MLR algorithms, query-document pairs are represented by numerical vectors. Such an approach is sometimes called bag of features and is analogous to the bag of words model, components of such vectors are called features, factors or ranking signals. They may be divided into three groups, Query-independent or static features — those features, which only on the document. For example, PageRank or documents length, such features can be precomputed in off-line mode during indexing. They may be used to compute documents static quality score, which is used to speed up search query evaluation. Query-dependent or dynamic features — those features, which depend both on the contents of the document and the query, such as TF-IDF score or other non-machine-learned ranking functions, query level features or query features, which depend only on the query. For example, the number of words in a query, selecting and designing good features is an important area in machine learning, which is called feature engineering. There are several measures which are used to judge how well an algorithm is doing on training data
19.
Orthogonality
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The concept of orthogonality has been broadly generalized in mathematics, as well as in areas such as chemistry, and engineering. The word comes from the Greek ὀρθός, meaning upright, and γωνία, the ancient Greek ὀρθογώνιον orthogōnion and classical Latin orthogonium originally denoted a rectangle. Later, they came to mean a right triangle, in the 12th century, the post-classical Latin word orthogonalis came to mean a right angle or something related to a right angle. In geometry, two Euclidean vectors are orthogonal if they are perpendicular, i. e. they form a right angle, two vectors, x and y, in an inner product space, V, are orthogonal if their inner product ⟨ x, y ⟩ is zero. This relationship is denoted x ⊥ y, two vector subspaces, A and B, of an inner product space, V, are called orthogonal subspaces if each vector in A is orthogonal to each vector in B. The largest subspace of V that is orthogonal to a subspace is its orthogonal complement. Given a module M and its dual M∗, an element m′ of M∗, two sets S′ ⊆ M∗ and S ⊆ M are orthogonal if each element of S′ is orthogonal to each element of S. A term rewriting system is said to be if it is left-linear and is non-ambiguous. Orthogonal term rewriting systems are confluent, a set of vectors in an inner product space is called pairwise orthogonal if each pairing of them is orthogonal. Such a set is called an orthogonal set, nonzero pairwise orthogonal vectors are always linearly independent. In certain cases, the normal is used to mean orthogonal. For example, the y-axis is normal to the curve y = x2 at the origin, however, normal may also refer to the magnitude of a vector. In particular, a set is called if it is an orthogonal set of unit vectors. As a result, use of the normal to mean orthogonal is often avoided. The word normal also has a different meaning in probability and statistics, a vector space with a bilinear form generalizes the case of an inner product. When the bilinear form applied to two results in zero, then they are orthogonal. The case of a pseudo-Euclidean plane uses the term hyperbolic orthogonality, in the diagram, axes x′ and t′ are hyperbolic-orthogonal for any given ϕ. In 2-D or higher-dimensional Euclidean space, two vectors are orthogonal if and only if their dot product is zero, i. e. they make an angle of 90°, hence orthogonality of vectors is an extension of the concept of perpendicular vectors into higher-dimensional spaces
20.
Precision and recall
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Both precision and recall are therefore based on an understanding and measure of relevance. Suppose a computer program for recognizing dogs in photographs identifies eight dogs in a picture containing 12 dogs, of the 8 dogs identified,5 actually are dogs, while the rest are cats. The programs precision is 5/8 while its recall is 5/12, when a search engine returns 30 pages only 20 of which were relevant while failing to return 40 additional relevant pages, its precision is 20/30 = 2/3 while its recall is 20/60 = 1/3. So, in case, precision is how useful the search results are. In statistics, if the hypothesis is that all and only the relevant items are retrieved, absence of type I and type II errors corresponds respectively to maximum precision. The above pattern recognition example contained 8 −5 =3 type I errors and 12 −5 =7 type II errors, Precision can be seen as a measure of exactness or quality, whereas recall is a measure of completeness or quantity. In this case, the relevant documents are simply those that belong to the relevant category, in a classification task, the precision for a class is the number of true positives divided by the total number of elements labeled as belonging to the positive class. Recall in this context is defined as the number of true positives divided by the number of elements that actually belong to the positive class. Often, there is a relationship between precision and recall, where it is possible to increase one at the cost of reducing the other. Brain surgery provides an example of the tradeoff. Consider a brain surgeon tasked with removing a tumor from a patient’s brain. The surgeon needs to remove all of the cells since any remaining cancer cells will regenerate the tumor. Conversely, the surgeon must not remove healthy brain cells since that would leave the patient with impaired brain function, the surgeon may be more liberal in the area of the brain she removes to ensure she has extracted all the cancer cells. This decision increases recall but reduces precision, on the other hand, the surgeon may be more conservative in the brain she removes to ensure she extracts only cancer cells. This decision increases precision but reduces recall and that is to say, greater recall increases the chances of removing healthy cells and increases the chances of removing all cancer cells. Greater precision decreases the chances of removing healthy cells but also decreases the chances of removing all cancer cells, usually, precision and recall scores are not discussed in isolation. Instead, either values for one measure are compared for a level at the other measure or both are combined into a single measure. Accuracy is an arithmetic mean of Precision and Inverse Precision as well as a weighted arithmetic mean of Recall
21.
Joseph Marie Jacquard
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Joseph Marie Charles dit Jacquard was a French weaver and merchant. In his grandfather’s generation, several branches of the Charles family lived in Lyon’s Couzon-Au-Mont d’Or suburb, to distinguish the various branches, the community gave them nicknames, Joseph’s branch was called “Jacquard” Charles. Thus, Joseph’s grandfather was Bartholomew Charles dit Jacquard, Joseph Marie Charles sit Jacquard was born into a conservative catholic family in Lyon, France on 7 July 1752. He was one of nine children of Jean Charles sit Jacquard, a weaver of Lyon. However, only Joseph and his sister Clemenceau survived to adulthood, although his father was a man of property, Joseph received no formal schooling and remained illiterate until he was 13. He was finally taught by his brother-in-law, Jean-Marie Barrett, who ran a printing, Barrett also introduced Joseph to learned societies and scholars. His mother died in 1762, and when his father died in 1772, Joseph inherited his father’s house, looms and workshop as well as a vineyard, Joseph then dabbled in real estate. In 1778, he listed his occupations as master weaver and silk merchant, there is some confusion about Jacquard’s early work history. British economist Sir John Bowring met Jacquard, who told Bowring that at one time he had been a maker of straw hats. Eymard claimed that before becoming involved in the weaving of silk, Jacquard was a type-founder, a soldier, a bleacher of straw hats, Barlow claims that before marrying, Jacquard had worked for a bookbinder, a type-founder, and a maker of cutlery. After marrying, Jacquard tried cutlery making, type-founding, and weaving, however, Barlow does not cite any sources for that information. Ballot stated that Jacquard initially helped his father operate his loom, on 26 July 1778, Joseph married Claudine Boichon. She was a widow from Lyon who owned property and had a substantial dowry. However, Joseph soon fell deeply into debt and was brought to court, Barlow claims that after Jacquards father died, Jacquard started a figure-weaving business but failed and lost all his wealth. However, Barlow cites no sources to support his claim, to settle his debts, he was obliged to sell his inheritance and to appropriate his wife’s dowry. Fortunately, his wife retained a house in Oullins, where the couple resided, on 19 April 1779, the couple had their only child, a son, Jean Marie. Beyond his name and his date of birth, nothing is known about Jacquards son, Charles Ballot stated that after the rebellion of Lyon in 1793 was suppressed, Jacquard and his son escaped from the city by joining the revolutionary army. They fought together in the Rhine campaign of 1795, serving in the Rhone-and-Loire battalion under General Jean Charles Pichegru, Jacquards son was killed outside of Heidelberg
22.
Jacquard loom
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The Jacquard machine is a device fitted to a power loom that simplifies the process of manufacturing textiles with such complex patterns as brocade, damask and matelassé. It was invented by Joseph Marie Jacquard in 1804, the loom was controlled by a chain of cards, a number of punched cards, laced together into a continuous sequence. Multiple rows of holes were punched on each card, with one complete card corresponding to one row of the design, several such paper cards, generally white in color, can be seen in the images below. Chains, like Bouchons earlier use of tape, allowed sequences of any length to be constructed. It is based on earlier inventions by the Frenchmen Basile Bouchon, Jean Baptiste Falcon, a static display of a Jacquard loom is the centrepiece of the Musée des Tissus et des Arts décoratifs in Lyon. Live displays of a Jacquard loom are available at a few museums around Lyon and also twice a day at La Maison des Canuts. Both the Jacquard process and the necessary loom attachment are named after their inventor and this mechanism is probably one of the most important weaving inventions as Jacquard shedding made possible the automatic production of unlimited varieties of pattern weaving. The term Jacquard is not specific or limited to any particular loom, the process can also be used for patterned knitwear and machine-knitted textiles, such as jerseys. This use of punched cards to control a sequence of operations is considered an important step in the history of computing hardware. In former times, if figured designs were required, this was done on a drawloom, the heddles with warp ends to be pulled up were manually selected by a second operator, apart from the weaver. It was slow and labour-intensive, with limitations on the complexity of the pattern. The first important improvement of the draw took place in 1725. A continuous roll of paper was punched by hand, in sections, each of which represented one lash or tread, the Jacquard machine subsequently evolved from this. It is difficult to determine part of the Jacquard machine was designed by Jacquard himself. He may have combined the best mechanical elements of other inventors, Jacquards machine also contained eight rows of needles and uprights as compared with Vaucansons double row, of which modifications enabled him to increase the figuring capacity of the machine. In his first machine, he supported the harness by knotted cords, Jacquards invention had a deep influence on Charles Babbage. In that respect, he is viewed by some authors as a precursor of modern computing science. Each position in the card corresponds to a Bolus hook, which can either be raised or stopped dependent on whether the hole is punched out of the card or the card is solid
23.
Herman Hollerith
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Herman Hollerith was an American inventor who developed an electromechanical punched card tabulator to assist in summarizing information and, later, accounting. He was the founder of the Tabulating Machine Company that was amalgamated in 1911 with three companies to form a fifth company, the Computing-Tabulating-Recording Company later renamed IBM. Hollerith is regarded as one of the figures in the development of data processing. His invention of the punched card tabulating machine marks the beginning of the era of data processing systems. Herman Hollerith was born the son of German immigrant Prof. Georg Hollerith from Großfischlingen in Buffalo, New York, in 1882 Hollerith joined the Massachusetts Institute of Technology where he taught mechanical engineering and conducted his first experiments with punched cards. He died in Washington D. C. of a heart attack, at the urging of John Shaw Billings, Hollerith developed a mechanism using electrical connections to increment a counter, recording information. A key idea was that a datum could be recorded by the presence or absence of a hole at a location on a card. For example, if a hole location indicates marital status. Hollerith determined that data in specified locations on a card, the rows and columns. A description of system, An Electric Tabulating System, was submitted by Hollerith to Columbia University as his doctoral thesis. On January 8,1889, Hollerith was issued U. S. Hollerith had left teaching, titled Art of Compiling Statistics, it was filed on September 23,1884, U. S. Patent 395,782 was granted on January 8,1889, Hollerith initially did business under his own name, as The Hollerith Electric Tabulating System, specializing in punched card data processing equipment. He built tabulators and other machines under contract for the Census Office, in 1896 Hollerith founded the Tabulating Machine Company. Many major census bureaus around the world leased his equipment and purchased his cards, as did major insurance companies. Holleriths machines were used for censuses in England, Italy, Germany, Russia, Austria, Canada, France, Norway, Puerto Rico, Cuba, and the Philippines and he invented the first automatic card-feed mechanism and the first keypunch. The 1890 Tabulator was hardwired to operate on 1890 Census cards, a control panel in his 1906 Type I Tabulator simplified rewiring for different jobs. The 1920s removable control panel supported prewiring and near instant job changing and these inventions were among the foundations of the data processing industry and Holleriths punched cards continued in use for almost a century. In 1911 four corporations, including Holleriths firm, were amalgamated to form a fifth company, under the presidency of Thomas J. Watson, CTR was renamed International Business Machines Corporation in 1924
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Punched card
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A punched card or punch card is a piece of stiff paper that can be used to contain digital information represented by the presence or absence of holes in predefined positions. The information might be data for data processing applications or, in earlier examples, the terms IBM card, or Hollerith card specifically refer to punched cards used in semiautomatic data processing. Many early digital computers used punched cards, often prepared using keypunch machines, while punched cards are now obsolete as a recording medium, as of 2012, some voting machines still use punched cards to record votes. Basile Bouchon developed the control of a loom by punched holes in paper tape in 1725, in 1801 Joseph Marie Jacquard demonstrated a mechanism to automate loom operation. A number of punched cards were linked into a chain of any length, each card held the instructions for shedding and selecting the shuttle for a single pass. It is considered an important step in the history of computing hardware, semen Korsakov was reputedly the first to use the punched cards in informatics for information store and search. Korsakov announced his new method and machines in September 1832, rather than seeking patents, charles Babbage proposed the use of Number Cards, pierced with certain holes and stand opposite levers connected with a set of figure wheels. Advanced they push in those levers opposite to which there are no holes on the card, Herman Hollerith invented the recording of data on a medium that could then be read by a machine. Prior uses of machine readable media, such as those above, had been for control, after some initial trials with paper tape, he settled on punched cards. Developing punched card data processing technology for the 1890 US census, other companies entering the punched card business included the Powers Accounting Machine Company, Remington Rand, and Groupe Bull. Both IBM and Remington Rand tied punched card purchases to machine leases, in 1932, the US government took both to court on this issue. IBM viewed its business as providing a service and that the cards were part of the machine, IBM fought all the way to the Supreme Court and lost in 1936, the court ruling that IBM could only set card specifications. According to the IBM Archives, By 1937, IBM had 32 presses at work in Endicott, N. Y. printing, cutting and stacking five to 10 million punched cards every day. Punched cards were used as legal documents, such as U. S. Government checks. Punched card technology developed into a tool for business data-processing. By 1950 punched cards had become ubiquitous in industry and government, do not fold, spindle or mutilate, a generalized version of the warning that appeared on some punched cards, became a motto for the post-World War II era. In 1955 IBM signed a consent decree requiring, amongst other things, tom Watson Jr. s decision to sign this decree, where IBM saw the punched card provisions as the most significant point, completed the transfer of power to him from Thomas Watson, Sr. The UNITYPER introduced magnetic tape for data entry in the 1950s, during the 1960s, the punched card was gradually replaced as the primary means for data storage by magnetic tape, as better, more capable computers became available
25.
Keypunch
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A keypunch is a device for precisely punching holes into stiff paper cards at specific locations as determined by keys struck by a human operator. For Jacquard looms, the punched cards were joined together to form a chain, equivalent to paper tape containing a program that. For Hollerith machines and other unit record machines the resulting punched cards contained data to be processed by those machines, for computers equipped with a punched card input/output device the resulting punched cards were either data or programs directing the computers operation. Early Hollerith keypunches were manual devices, later keypunches were electromechanical devices which combined several functions in one unit. These often resembled small desks with keyboards similar to those on typewriters and were equipped with hoppers for blank cards, some keypunch models could print, at the top of a column, the character represented by the hole punched in that column. The small pieces punched out by a keypunch fell into a box, or chip box. In many data processing applications, the cards were verified by keying exactly the same data a second time, checking to see if the second keying. Keypunches were popular through the 1970s but were made obsolete by changes in the data input paradigm. Jacquard cards were said to be stamped or cut, the first Jacquard cards were stamped by hand, sometimes using a guide plate. An improvement was to place the card between two perforated plates, insert punches according to the desired pattern, then pass the assembly through a press to cut the card. These essentially manual processes were replaced by machines, piano machines, operated by keyboards and comparable in function to unit record keypunches, herman Holleriths first device for punching cards from the 1890s was. any ordinary ticket punch, cutting a round hole 3/16 of an inch in diameter. Use of such a punch was facilitated by placing the holes to be used near the edges of the card, in 1901 Hollerith patented a mechanism where an operator pressed one of 12 keys to punch a hole, with the card automatically advancing to the next column. This first-generation Type 001 keypunch used 45 columns and round holes, the IBM80 column punched card format was introduced in 1928, existing keypunches and other products were modified for this new format while keeping the same model numbers. Later IBM keypunches included the Type 016 Motor-Driven Electric Duplicating Keypunch, the Type 31 Alphabetical Duplicating Punch, the Type 31 Alphabetical Duplicating Punch was introduced by IBM in 1933, and it automatically ejected one card and fed another in 0.65 second. These machines were equipped with separate alphabetical and numerical keyboards, – IBM Archives Most IBM keypunch and verifiers used a common electrical/mechanical design in their keyboards to encode the mechanical keystrokes. As a key was depressed, a link on the keystem tripped a corresponding set of bails at the top of the keyboard assembly, the bails in turn made contacts to encode the characters electrically. The keyboards, however, had no provision for adjusting the other than the correct adjustment of the contacts on the restore bail contacts. Special function keys such as shift, release, duplication and others, had only electrical contacts under their stems, with no mechanical linkage to the bail assembly for encoding
26.
Tabulating machine
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The tabulating machine was an electromechanical machine designed to assist in summarizing information stored on punched cards. Invented by Herman Hollerith, the machine was developed to process data for the 1890 U. S. Census. Later models were used for business applications such as accounting. It spawned a class of machines, known as unit record equipment, the term Super Computing was used by the New York World newspaper in 1931 to refer to a large custom-built tabulator that IBM made for Columbia University. The 1880 census had taken eight years to process, constitution mandates a census every ten years to apportion both congressional representatives and direct taxes among the states, a larger staff or faster method was required. Prior uses of machine readable media had been for lists of instructions to drive programmed machines such as Jacquard looms, after some initial trials with paper tape, he settled on punched cards. Hollerith used punched cards with holes,12 rows and 24 columns. His tabulator used electromechanical relays to increment mechanical counters, a set of spring-loaded wires were suspended over the card reader. The card sat over pools of mercury, pools corresponding to the hole positions in the card. This arrangement allowed a count up to 10,000, during a given tabulating run, counters could be assigned a specific hole or, using relay logic, a combination of holes, e. g. to count married females. If the card was to be sorted a compartment lid of the box would open for storage of the card. Holleriths method was used for the 1890 census, clerks used keypunches to punch holes in the cards entering age, state of residence, gender, and other information from the returns. The advantages of the technology were immediately apparent for accounting and tracking inventory, Hollerith started his own business as The Hollerith Electric Tabulating System, specializing in punched card data processing equipment. In 1896 he incorporated the Tabulating Machine Company, in that year he introduced the Hollerith Integrating Tabulator, which could add numbers coded on punched cards, not just count the number of holes. Punched cards were still read using the pins and mercury pool reader. 1900 saw the Hollerith Automatic Feed Tabulator used in that years U. S. census, a control panel was incorporated in the 1906 Type 1. In 1911, four corporations, including Holleriths firm, were amalgamated to form a fifth company, the Powers Accounting Machine Company was formed that same year and, like Hollerith, with machines first developed at the Census Bureau. In 1919 the first Bull tabulator prototype was developed, tabulators that could print, and with removable control panels, appeared in the 1920s
