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Precision-time tradeoffs: A paradigm for processing statistical queries on databases

  • Jaideep Srivastava
  • Doron Rotem
Contributed Papers
  • 115 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 339)

Abstract

Conventional query processing techniques are aimed at queries which access small amounts of data, and require each data item for the answer. In case the database is used for statistical analysis as well as operational purposes, for some types of queries a large part of the database may be required to compute the answer. This may lead to a data access bottleneck, caused by the excessive number of disk accesses needed to get the data into primary memory. An example is computation of statistical parameters, such as count, average, median, and standard deviation, which are useful for statistical analysis of the database. Yet another example that faces this bottleneck is the verification of the truth of a set of predicates (goals), based on the current database state, for the purposes of intelligent decision making. A solution to this problem is to maintain a set of precomputed information about the database in a view or a snapshot. Statistical queries can be processed using the view rather than the real database. A crucial issue is that the precision of the precomputed information in the view deteriorates with time, because of the dynamic nature of the underlying database. Thus the answer provided is approximate, which is acceptable under many circumstances, especially when the error is bounded. The tradeoff is that the processing of queries is made faster at the expense of the precision in the answer. The concept of precision in the context of database queries is formalized, and a data model to incorporate it is developed. Algorithms are designed to maintain materialized views of data to specified degrees of precision.

Keywords

Data Item Relational Algebra Data Copy Query Plan Disk Access 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1989

Authors and Affiliations

  • Jaideep Srivastava
    • 1
  • Doron Rotem
    • 1
  1. 1.Computer Science Research Lawrence Berkeley LaboratoryUniversity of CaliforniaBerkeley

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