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Data Reduction Workflow Patterns Mining and Optimization based on Ontology

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Published:21 December 2018Publication History

ABSTRACT

Focusing on massive high-dimensional data reduction, this paper established the ontology model of data reduction, to improve the related theoretical methods of data reduction, and to propose an ontology-based data reduction system architecture. Data reduction workflow model, workflow pattern mining, and workflow optimization and data reduction experiment design based on knowledge base are studied to achieve the accumulation, sharing and reuse of data reduction knowledge and enhance the intelligence level of data reduction process as well as the credibility of reduction results. A mechanism of meta-reduction system framework and its technology is put forward so as to improve the availability, flexibility and applicability of data reduction system.

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    • Published in

      cover image ACM Other conferences
      ACAI '18: Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence
      December 2018
      460 pages
      ISBN:9781450366250
      DOI:10.1145/3302425

      Copyright © 2018 ACM

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      Publication History

      • Published: 21 December 2018

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      ACAI '18 Paper Acceptance Rate76of192submissions,40%Overall Acceptance Rate173of395submissions,44%
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