Data integration with uncertainty

  • Authors:

  • Xin Dong;Alon Y. Halevy;Cong Yu

  • Affiliations:

  • University of Washington, Seattle, WA;Google Inc., Mountain View, CA;University of Michigan, Ann Arbor, MI

  • Venue:
  • VLDB '07 Proceedings of the 33rd international conference on Very large data bases
  • Year:
  • 2007

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Abstract

This paper reports our first set of results on managing uncertainty in data integration. We posit that data-integration systems need to handle uncertainty at three levels, and do so in a principled fashion. First, the semantic mappings between the data sources and the mediated schema may be approximate because there may be too many of them to be created and maintained or because in some domains (e.g., bioinformatics) it is not clear what the mappings should be. Second, queries to the system may be posed with keywords rather than in a structured form. Third, the data from the sources may be extracted using information extraction techniques and so may yield imprecise data. As a first step to building such a system, we introduce the concept of probabilistic schema mappings and analyze their formal foundations. We show that there are two possible semantics for such mappings: by-table semantics assumes that there exists a correct mapping but we don't know what it is; by-tuple semantics assumes that the correct mapping may depend on the particular tuple in the source data. We present the query complexity and algorithms for answering queries in the presence of approximate schema mappings, and we describe an algorithm for efficiently computing the top-k answers to queries in such a setting.