Download Dan`s slides - Purdue University

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
Tables stored in relational database
• Tables  Events (= Probabilistic tables)
Expressive probabilistic model
• Maybe/Or tuples
• Views over events
• Confidences for views
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)
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
Using MystiQ
• Store data in RDBMS (demo: postgres)
• Write a configuration file
• Run SQL queries on MystiQ
Demo
Views
later
• Standard:
Tables  Tables (  Events )
• Probabilistic:
Events  Events
A BN in MystiQ
Color
Shape
Color
Shape
Weight
Red
Blue
Round
Weight
prob
Light
0.3
Medium 0.7
Heavy
0.2
Light
0.1
Square
Medium 0.4
Applying BN to a Table
Product(prod,price,color,shape,prob)
Prod
Color
Shape
Camera
Red
Round
Camera
Blue
Square
Weight
Light
Medium
Heavy
Light
Medium
ProductEvent(prod,price,color,shape)
prob
0.3
0.7
0.2
0.1
0.4
Applications of ProbDB ?
• Fuzzy object matching: IMDB + AMZN
• Information extraction
• What else ???
Development
• Developed under a TGIF grant
• Free license (on request) for research
institutions
Current/Future Work
• Constraint, Data mappings
• Theory of conjunctive queries on probdb
• Cleaning of sensor data (w/ Balazinska)