Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A linear time algorithm for finding tree-decompositions of small treewidth
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
An optimal approximation algorithm for Bayesian inference
Artificial Intelligence
An Introduction to Variational Methods for Graphical Models
Machine Learning
Optimal ordered binary decision diagrams for read-once formulas
Discrete Applied Mathematics
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Efficient reasoning in graphical models
Efficient reasoning in graphical models
MYSTIQ: a system for finding more answers by using probabilities
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Management of probabilistic data: foundations and challenges
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The dichotomy of conjunctive queries on probabilistic structures
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Counting truth assignments of formulas of bounded tree-width or clique-width
Discrete Applied Mathematics
MCDB: a monte carlo approach to managing uncertain data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Conditioning probabilistic databases
Proceedings of the VLDB Endowment
Fast and Simple Relational Processing of Uncertain Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Exploiting Lineage for Confidence Computation in Uncertain and Probabilistic Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
SPROUT: Lazy vs. Eager Query Plans for Tuple-Independent Probabilistic Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
MayBMS: a probabilistic database management system
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Combining intensional with extensional query evaluation in tuple independent probabilistic databases
Information Sciences: an International Journal
Read-once functions and query evaluation in probabilistic databases
Proceedings of the VLDB Endowment
Faster query answering in probabilistic databases using read-once functions
Proceedings of the 14th International Conference on Database Theory
Local structure and determinism in probabilistic databases
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
On the tractability of query compilation and bounded treewidth
Proceedings of the 15th International Conference on Database Theory
Oblivious bounds on the probability of boolean functions
ACM Transactions on Database Systems (TODS)
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There are two broad approaches to query evaluation over probabilistic databases: (1) Intensional Methods proceed by manipulating expressions over symbolic events associated with uncertain tuples. This approach is very general and can be applied to any query, but requires an expensive postprocessing phase, which involves some general-purpose probabilistic inference. (2) Extensional Methods, on the other hand, evaluate the query by translating operations over symbolic events to a query plan; extensional methods scale well, but they are restricted to safe queries. In this paper, we bridge this gap by proposing an approach that can translate the evaluation of any query into extensional operators, followed by some post-processing that requires probabilistic inference. Our approach uses characteristics of the data to adapt smoothly between the two evaluation strategies. If the query is safe or becomes safe because of the data instance, then the evaluation is completely extensional and inside the database. If the query/data combination departs from the ideal setting of a safe query, then some intensional processing is performed, whose complexity depends only on the distance from the ideal setting.