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  • × author_ss:"Benoit, G."
  1. Benoit, G.; Hussey, L.: Repurposing digital objects : case studies across the publishing industry (2011) 0.03 
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    Abstract
    Large, data-rich organizations have tremendously large collections of digital objects to be "repurposed," to respond quickly and economically to publishing, marketing, and information needs. Some management typically assume that a content management system, or some other technique such as OWL and RDF, will automatically address the workflow and technical issues associated with this reuse. Four case studies show that the sources of some roadblocks to agile repurposing are as much managerial and organizational as they are technical in nature. The review concludes with suggestions on how digital object repurposing can be integrated given these organizations' structures.
    Date
    22. 1.2011 14:23:07
    Type
    a
  2. Benoit, G.: Data discretization for novel relationship discovery in information retrieval (2002) 0.00 
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    Abstract
    A sample of 600 Dialog and Swiss-Prot full text records in genetics and molecular biology were parsed and term frequencies calculated to provide data for a test of Benoit's visualization model for retrieval. A retrieved set is displayed graphically allowing for manipulation of document and concept relationships in real time, which hopefully will reveal unanticipated relationships.
    Type
    a
  3. Benoit, G.: Data mining (2002) 0.00 
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    Abstract
    Data mining (DM) is a multistaged process of extracting previously unanticipated knowledge from large databases, and applying the results to decision making. Data mining tools detect patterns from the data and infer associations and rules from them. The extracted information may then be applied to prediction or classification models by identifying relations within the data records or between databases. Those patterns and rules can then guide decision making and forecast the effects of those decisions. However, this definition may be applied equally to "knowledge discovery in databases" (KDD). Indeed, in the recent literature of DM and KDD, a source of confusion has emerged, making it difficult to determine the exact parameters of both. KDD is sometimes viewed as the broader discipline, of which data mining is merely a component-specifically pattern extraction, evaluation, and cleansing methods (Raghavan, Deogun, & Sever, 1998, p. 397). Thurasingham (1999, p. 2) remarked that "knowledge discovery," "pattern discovery," "data dredging," "information extraction," and "knowledge mining" are all employed as synonyms for DM. Trybula, in his ARIST chapter an text mining, observed that the "existing work [in KDD] is confusing because the terminology is inconsistent and poorly defined.
    Type
    a

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