Search (99 results, page 1 of 5)

  • × theme_ss:"Retrievalalgorithmen"
  1. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.04 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  2. Wilbur, W.J.: ¬A retrieval system based on automatic relevance weighting of search terms (1992) 0.03 
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    Imprint
    Medford, NJ : Learned Information Inc.
  3. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.03 
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    Date
    14. 6.2015 22:12:44
    Source
    Deutscher Dokumentartag 1985, Nürnberg, 1.-4.10.1985: Fachinformation: Methodik - Management - Markt; neue Entwicklungen, Berufe, Produkte. Bearb.: H. Strohl-Goebel
  4. Deerwester, S.; Dumais, S.; Landauer, T.; Furnass, G.; Beck, L.: Improving information retrieval with latent semantic indexing (1988) 0.02 
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    Imprint
    Medford, New Jersey : Learned Information Inc.
  5. Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.02 
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    Abstract
    We address how individuals' (workers) knowledge needs influence the design of knowledge management systems (KMS), enabling knowledge creation and utilization. It is evident that KMS technologies and activities are indiscriminately deployed in most organizations with little regard to the actual context of their adoption. Moreover, it is apparent that the extant literature pertaining to knowledge management projects is frequently deficient in identifying the variety of factors indicative for successful KMS. This presents an obvious business practice and research gap that requires a critical analysis of the necessary intervention that will actually improve how workers can leverage and form organization-wide knowledge. This research involved an extensive review of the literature, a grounded theory methodological approach and rigorous data collection and synthesis through an empirical case analysis (Parsons Brinckerhoff and Samsung). The contribution of this study is the formulation of a model for designing KMS based upon the design science paradigm, which aspires to create artifacts that are interdependent of people and organizations. The essential proposition is that KMS design and implementation must be contextualized in relation to knowledge needs and that these will differ for various organizational settings. The findings present valuable insights and further understanding of the way in which KMS design efforts should be focused.
    Date
    11. 6.2012 14:22:34
  6. Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.01 
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    Date
    20. 1.2015 18:30:22
    18. 9.2018 18:22:56
    Source
    Aslib journal of information management. 67(2015) no.6, S.700-714
  7. Witschel, H.F.: Global term weights in distributed environments (2008) 0.01 
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    Date
    1. 8.2008 9:44:22
    Source
    Information processing and management. 44(2008) no.3, S.1049-1061
  8. Efthimiadis, E.N.: User choices : a new yardstick for the evaluation of ranking algorithms for interactive query expansion (1995) 0.01 
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    Date
    22. 2.1996 13:14:10
    Source
    Information processing and management. 31(1995) no.4, S.605-620
  9. Salton, G.: ¬A simple blueprint for automatic Boolean query processing (1988) 0.01 
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    Source
    Information processing and management. 24(1988) no.3, S.269-280
  10. Reddaway, S.: High speed text retrieval from large databases on a massively parallel processor (1991) 0.01 
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    Source
    Information processing and management. 27(1991), S.311-316
  11. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.01 
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    Date
    30. 3.2001 13:32:22
  12. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.01 
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    Date
    25. 8.2005 17:42:22
  13. Daniowicz, C.; Baliski, J.: Document ranking based upon Markov chains (2001) 0.01 
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    Information processing and management. 37(2001) no.4, S.623-637
  14. Horng, J.T.; Yeh, C.C.: Applying genetic algorithms to query optimization in document retrieval (2000) 0.01 
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    Information processing and management. 36(2000) no.5, S.737-759
  15. Ciocca, G.; Schettini, R.: ¬A relevance feedback mechanism for content-based image retrieval (1999) 0.01 
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    Information processing and management. 35(1999) no.5, S.605-632
  16. Niemi, T.; Junkkari, M.; Järvelin, K.; Viita, S.: Advanced query language for manipulating complex entities (2004) 0.01 
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    Information processing and management. 40(2004) no.6, S.869-
  17. Koyama, M.: ¬A fast retrieving algorithm of hierarchical relationships using tree structures (1998) 0.01 
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    Information processing and management. 34(1998) no.6, S.761-763
  18. Lee, J.H.: Combining the evidence of different relevance feedback methods for information retrieval (1998) 0.01 
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    Information processing and management. 34(1998) no.6, S.681-691
  19. Clarke, C.L.A.; Cormack, G.V.; Tudhope, E.A.: Relevance ranking for one to three term queries (2000) 0.01 
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    Information processing and management. 36(2000) no.2, S.291-311
  20. Chung, Y.M.; Lee, J.Y.: Optimization of some factors affecting the performance of query expansion (2004) 0.01 
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    Information processing and management. 40(2004) no.6, S.891-

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