Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
ProbView: a flexible probabilistic database system
ACM Transactions on Database Systems (TODS)
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
MYSTIQ: a system for finding more answers by using probabilities
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Indexing multi-dimensional uncertain data with arbitrary probability density functions
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Efficient join processing over uncertain data
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
ProTDB: probabilistic data in XML
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Orion 2.0: native support for uncertain data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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Applications requiring the handling of uncertain data have led to the development of database management systems extending the scope of relational databases to include uncertain (probabilistic) data as a native data type. New automatic query optimizations having the ability to estimate the cost of execution of a given query plan, as available in existing databases, need to be developed. For probabilistic data this involves providing selectivity estimations that can handle multiple values for each attribute and also new query types with threshold values. This paper presents novel selectivity estimation functions for uncertain data and shows how these functions can be integrated into PostgreSQL to achieve query optimization for probabilistic queries over uncertain data. The proposed methods are able to handle both attribute- and tuple-uncertainty. Our experimental results show that our algorithms are efficient and give good selectivity estimates with low space-time overhead.