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Beauty vs. the beast: The case against massively parallel retrieval

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Progress in Case-Based Reasoning (UK CBR 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1020))

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Abstract

One of the most researched areas of CBR is the task of case retrieval. Several, well-established techniques, such as indexing and similarity-based retrieval, have proven sufficient for relatively small-scale, single-purpose applications. In this paper, a comparative review of different retrieval techniques is given. Memory-driven retrieval is seen as being a new, more powerful and flexible approach though correspondingly the most difficult. Massively parallel marker passing is identified as an appealing implementation medium for memory-driven retrieval. However, this paper questions both the necessity and sufficiency of a brute-force solution alone. Rather, the combination of explicit use of “context” and statistical measures is shown, by example, to allow many retrieval problems to be potentially solved on serial architectures. The paper concludes with a proposal for a general-purpose, hybrid retrieval

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Authors and Affiliations

  1. Department. of Computer Science, University of Manchester, Oxford Road, M13 9PL, Manchester, UK

    Dr Mike Brown & Dr Nick Filer

Authors
  1. Dr Mike Brown
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  2. Dr Nick Filer
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Editor information

Ian D. Watson

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© 1995 Springer-Verlag Berlin Heidelberg

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Brown, M., Filer, N. (1995). Beauty vs. the beast: The case against massively parallel retrieval. In: Watson, I.D. (eds) Progress in Case-Based Reasoning. UK CBR 1995. Lecture Notes in Computer Science, vol 1020. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60654-8_21

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  • DOI: https://doi.org/10.1007/3-540-60654-8_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60654-3

  • Online ISBN: 978-3-540-48525-4

  • eBook Packages: Springer Book Archive

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