MYSTIQ: a system for finding more answers by using probabilities

  • Authors:

  • Jihad Boulos;Nilesh Dalvi;Bhushan Mandhani;Shobhit Mathur;Chris Re;Dan Suciu

  • Affiliations:

  • American University of Beirut, Lebanon;University of Washington;American University of Beirut, Lebanon;American University of Beirut, Lebanon;American University of Beirut, Lebanon;American University of Beirut, Lebanon

  • Venue:
  • Proceedings of the 2005 ACM SIGMOD international conference on Management of data
  • Year:
  • 2005

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Abstract

MystiQ is a system that uses probabilistic query semantics [3] to find answers in large numbers of data sources of less than perfect quality. There are many reasons why the data originating from many different sources may be of poor quality, and therefore difficult to query: the same data item may have different representation in different sources; the schema alignments needed by a query system are imperfect and noisy; different sources may contain contradictory information, and, in particular, their combined data may violate some global integrity constraints; fuzzy matches between objects from different sources may return false positives or negatives. Even in such environment, users some-times want to ask complex, structurally rich queries, using query constructs typically found in SQL queries: joins, subqueries, existential/universal quantifiers, aggregate and group-by queries: for example scientists may use such queries to query multiple scientific data sources, or a law enforcement agency may use it in order to find rare associations from multiple data sources. If standard query semantics were applied to such queries, all but the most trivial queries will return an empty answer.