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MystiQ

MystiQ. The HusQies*. *Nilesh Dalvi, Brian Harris, Chris Re, Dan Suciu University of Washington. Outline. Overview Demo / discussions Conclusions. MystiQ. General purpose probabilistic database system Motivation: manage imprecisions in data. What MystiQ Does.

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MystiQ

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  1. MystiQ The HusQies* *Nilesh Dalvi, Brian Harris, Chris Re, Dan Suciu University of Washington

  2. Outline • Overview • Demo / discussions • Conclusions

  3. MystiQ • General purpose probabilistic database system • Motivation: manage imprecisions in data

  4. What MystiQ Does Tables stored in relational database • Tables  Events (= Probabilistic tables) Expressive probabilistic model • Maybe/Or tuples • Views over events • Confidences for views

  5. What MystiQ Does • Query semantics: • SQL: joins, distinct, aggregates/group-by • Point probabilities • Top-k answers, guaranteed ranking • Query evaluation • Safe plans • Monte Carlo simulation (Luby-Karp)

  6. What MystiQ Does Not • No syntax for popular probabilistic models • BNs, PRMs, rules with confidences • Can be expressed but indirectly • No lineage • No probabilities on continuous values

  7. Using MystiQ • Store data in RDBMS (demo: postgres) • Write a configuration file • Run SQL queries on MystiQ

  8. Demo

  9. Views later • Standard:Tables  Tables (  Events ) • Probabilistic:Events  Events

  10. A BN in MystiQ Color Shape Weight

  11. Applying BN to a Table Product(prod,price,color,shape,prob) ProductEvent(prod,price,color,shape)

  12. Applications of ProbDB ? • Fuzzy object matching: IMDB + AMZN • Information extraction • What else ???

  13. Development • Developed under a TGIF grant • Free license (on request) for research institutions

  14. Current/Future Work • Constraint, Data mappings • Theory of conjunctive queries on probdb • Cleaning of sensor data (w/ Balazinska)

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