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Probabilistic Databases
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    Format:
    Bog, paperback
    Udgivelsesdato:
    01-06-2011
    Sprog:
    Engelsk
    Sidetal:
    182
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    1. Beskrivelse

      Presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases. It then discusses two classes of techniques for query evaluation on probabilistic databases.

      Andre udgaver:

      E-bog, PDF

    2. Yderligere info
      ISBN13:
      9781608456802
      Vægt:
      324 g
      Dybde:
      9 mm
      Bredde:
      187 mm
      Højde:
      235 mm
      Format:
      Paperback
      • Bibliotekernes beskrivelse

        Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database.This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases.

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