MystiQ

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MystiQ

• The HusQies

Nilesh Dalvi, Brian Harris, Chris Re, Dan Suciu
University of Washington
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Outline

• Overview
• Demo / discussions
• Conclusions

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MystiQ

• General purpose probabilistic database system
• Motivation manage imprecisions in data

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What MystiQ Does

• Tables stored in relational database
• Tables ? Events ( Probabilistic tables)
• Expressive probabilistic model
• Maybe/Or tuples
• Views over events
• Confidences for views

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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)

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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

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Using MystiQ

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

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Probabilistic Tables Events
Product(prod,price,color,shape,prob)
ProductEvent(prod,price,color,shape)
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Configuration File

• Tables ? Events ( Probabilistic tables)

CREATE TABLE Product(prod, color, shape,
prob) CREATE EVENT ProductEvent(prod)
choice(color, shape)
ON Product(prob)
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Demo
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Views
later

• StandardTables ? Tables ( ? Events )
• ProbabilisticEvents ? Events

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A BN in MystiQ
Color
Shape
Weight
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Applying BN to a Table
Product(prod,price,color,shape,prob)
ProductEvent(prod,price,color,shape)
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Applications of ProbDB ?

• Fuzzy object matching IMDB AMZN
• Information extraction
• What else ???

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Development

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

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Current/Future Work

• Constraint, Data mappings
• Theory of conjunctive queries on probdb
• Cleaning of sensor data (w/ Balazinska)