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Austin, D.: PRECIS: Introduction (1987) 0.02

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Footnote: Zusammenfassende kompakte Darstellung des Systems PRECIS, seiner Geschichte, Eigenschaften und Möglichkeiten bis hin zum Online-Retrieval. - Als Einführung zu empfehlen
Source: Encyclopedia of library and information science. Vol.42, [=Suppl.7]

Austin, D.; Dale, P.: Guidelines for the establishment and development of monolingual thesauri (1981) 0.01

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Theme: Konzeption und Anwendung des Prinzips Thesaurus

Austin, D.: PRECIS: Grundprinzipien, Funktion und Anwendung (1983) 0.01

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Austin, D.; Sørensen, J.: Zusammenarbeit in der Entwicklung and Anwendung von PRECIS (1978) 0.01
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Abstract

Kurze Beschreibung des verbalen Indexierungssystems PRECIS, seiner Ursprünge und Entwicklung, seiner Bestandteile und seiner Ziele. Im einzelnen wird an Beispielen gezeigt, wie das Format einer PRECIS-Eintragung aussieht und wie die "Mechanik" der Eintragungsgenerierung mit Computerhilfe abläuft, wobei auch auf die Rollenoperatoren eingegangen wird. Abschließend eine Darstellung der Anwendung von PRECIS in anderen Sprachen, hier in Deutsch. An der Produktion von Eintragungen zum Austausch zwischen den 4 Sprachen Englisch, Französisch, Deutsch und Polnisch wird gegenwärtig gearbeitet. Untersuchungen für die skandinavischen Sprachen sind geplant

Austin, D.; Waters, J.: Guidelines for the establishment and development of multilingual thesauri (1980) 0.01

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Theme: Konzeption und Anwendung des Prinzips Thesaurus

Austin, D.: ¬The exchange of subject information (1975) 0.01

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Source: The interchange of bibliographic information in machine readable form. Ed.: R.E. Coward u. M. Yelland

Austin, D.: Vocabulary control and information technology (1986) 0.01

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Austin, D.: Development of a new general classification : a progress report (1969) 0.01

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Source: Information scientist. 3(1969) no.3, S.95-115

Austin, D.: Automatisierung in der Sacherschließung der British Library (1984) 0.01
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Abstract

Dieser Aufsatz beschäftigt sich mit Management-Aspekten der Sacherschließung in der British Library, Bibliographic Services Division, wo computergestützte, nicht völlig "automatische" Verfahren angewendet werden. In einer ausführlichen Darstellung des Arbeitsablaufes im Subject Systems Office wird der Weg eines Dokumentes durch die verschiedenen Sektionen verfolgt, und die betriebswirtschaftlichen Folgen der besonderen Rolle von PRECIS in diesem Arbeitsablauf werden erörtert. Das Mehrdateiensystem der British-Library-Datenbank wird beschrieben; es wird gezeigt, wie diese Struktur den effektiven Wiedergebrauch von Daten ermöglicht. Weiterhin wird die Verbesserung des on-line Retrieval durch den Einbau von präkoordinierten Themenangaben in den Suchablauf behandelt; abschließend wird die Rolle des Computers in der Sacherschließung einer IuD-Einrichtung wie der British Library diskutiert

Source

Bibliothek: Forschung und Praxis. 8(1984), S.45-57

Austin, D.: ¬The theory of integrative levels reconsidered as the basis of a general classification (1969) 0.00

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Source: Classification and information control. Papers representing the work of the Classification Research Group during 1960-1968

Austin, D.: PRECIS (2009) 0.00
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Abstract

Derek Austin's PRECIS, or PREserved Context Index System, was one of the most sophisticated indexing systems ever developed for text. Austin noted that there are many ways that the meaning of index entries may be misread and misunderstood, if the original context from which the concept came was not present. Drawing on a good understanding of linguistics, Austin and his colleagues developed a highly rigorous, yet not impossibly difficult, set of principles to use in indexing with the help of PRECIS. The system was particularly valuable for generating multiple word orderings of entries that still retained their proper contextual meaning. Much of the need for this feature was obviated by the development of powerful full text searching in information systems, however, and PRECIS receded from view in information science. The final sections of the article that address the details of coding and indexing management are deleted from the text below.

Source

Encyclopedia of library and information sciences. 3rd ed. Ed.: M.J. Bates

Austin, D.: Differences between library classifications and machine-based subject retrieval systems : some inferences drawn from research in Britain, 1963-1973 (1979) 0.00

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Source: Ordering systems for global information networks. Proc. of the 3rd Int. Study Conf. on Classification Research, Bombay 1975. Ed. by A. Neelameghan

Austin, D.: ¬A proposal for an International Standard Object Numberworks (1999) 0.00

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Imprint: Medford, NJ : Information Today
Series: Proceedings of the American Society for Information Science; vol.36
Source: Knowledge: creation, organization and use. Proceedings of the 62nd Annual Meeting of the American Society for Information Science, 31.10.-4.11.1999. Ed.: L. Woods

Austin, D.: How Google finds your needle in the Web's haystack : as we'll see, the trick is to ask the web itself to rank the importance of pages... (2006) 0.00
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Abstract

Imagine a library containing 25 billion documents but with no centralized organization and no librarians. In addition, anyone may add a document at any time without telling anyone. You may feel sure that one of the documents contained in the collection has a piece of information that is vitally important to you, and, being impatient like most of us, you'd like to find it in a matter of seconds. How would you go about doing it? Posed in this way, the problem seems impossible. Yet this description is not too different from the World Wide Web, a huge, highly-disorganized collection of documents in many different formats. Of course, we're all familiar with search engines (perhaps you found this article using one) so we know that there is a solution. This article will describe Google's PageRank algorithm and how it returns pages from the web's collection of 25 billion documents that match search criteria so well that "google" has become a widely used verb. Most search engines, including Google, continually run an army of computer programs that retrieve pages from the web, index the words in each document, and store this information in an efficient format. Each time a user asks for a web search using a search phrase, such as "search engine," the search engine determines all the pages on the web that contains the words in the search phrase. (Perhaps additional information such as the distance between the words "search" and "engine" will be noted as well.) Here is the problem: Google now claims to index 25 billion pages. Roughly 95% of the text in web pages is composed from a mere 10,000 words. This means that, for most searches, there will be a huge number of pages containing the words in the search phrase. What is needed is a means of ranking the importance of the pages that fit the search criteria so that the pages can be sorted with the most important pages at the top of the list. One way to determine the importance of pages is to use a human-generated ranking. For instance, you may have seen pages that consist mainly of a large number of links to other resources in a particular area of interest. Assuming the person maintaining this page is reliable, the pages referenced are likely to be useful. Of course, the list may quickly fall out of date, and the person maintaining the list may miss some important pages, either unintentionally or as a result of an unstated bias. Google's PageRank algorithm assesses the importance of web pages without human evaluation of the content. In fact, Google feels that the value of its service is largely in its ability to provide unbiased results to search queries; Google claims, "the heart of our software is PageRank." As we'll see, the trick is to ask the web itself to rank the importance of pages.

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