MYSTIQ

MYSTIQ: a system for finding more answers by using probabilities. 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.

This software is also peer reviewed by journal TOMS.


References in zbMATH (referenced in 11 articles )

Showing results 1 to 11 of 11.
Sorted by year (citations)

  1. Kimelfeld, Benny; Ré, Christopher: Transducing Markov sequences (2014)
  2. Qin, Biao; Wang, Shan: Combining intensional with extensional query evaluation in tuple independent probabilistic databases (2011)
  3. Das Sarma, Anish; Benjelloun, Omar; Halevy, Alon; Nabar, Shubha; Widom, Jennifer: Representing uncertain data: models, properties, and algorithms (2009) ioport
  4. Hassanzadeh, Oktie; Miller, Renée J.: Creating probabilistic databases from duplicated data (2009) ioport
  5. Sen, Prithviraj; Deshpande, Amol; Getoor, Lise: PRDB: managing and exploiting rich correlations in probabilistic databases (2009) ioport
  6. van Keulen, Maurice; de Keijzer, Ander: Qualitative effects of knowledge rules and user feedback in probabilistic data integration (2009) ioport
  7. Benjelloun, Omar; Das Sarma, Anish; Halevy, Alon; Theobald, Martin; Widom, Jennifer: Databases with uncertainty and lineage (2008) ioport
  8. Braga, Daniele; Campi, Alessandro; Ceri, Stefano; Raffio, Alessandro: Joining the results of heterogeneous search engines (2008) ioport
  9. Jeffery, Shawn R.; Franklin, Michael J.; Garofalakis, Minos: An adaptive RFID middleware for supporting metaphysical data independence (2008) ioport
  10. Magnani, Matteo; Montesi, Danilo: Management of interval probabilistic data (2008)
  11. Faber, Wolfgang; Greco, Gianluigi; Leone, Nicola: Magic Sets and their application to data integration (2007)