A probabilistic relational algebra for the integration of information retrieval and database systems
ACM Transactions on Information Systems (TOIS)
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Communications of the ACM
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ACM Computing Surveys (CSUR)
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CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
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AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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IEEE Transactions on Knowledge and Data Engineering
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Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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ICDCS '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems
Cardinality estimation and dynamic length adaptation for Bloom filters
Distributed and Parallel Databases
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Large amount of uncertain data is collected by many emerging applications which contain multiple sources in a distributed manner. Previous efforts on querying uncertain data in distributed environment have only focus on ranking and skyline, join queries have not been addressed in earlier work despite their importance in databases. In this paper, we address distributed probabilistic threshold join query, which retrieves results satisfying the join condition with combining probabilities that meet the threshold requirement from distributed sites. We propose a new kind of bloom filters called Probability Bloom Filters (PBF) to represent set with probabilistic attribute and design a PBF based Bloomjoin algorithm for executing distributed probabilistic threshold join query with communication efficiency. Furthermore, we provide theoretical analysis of the network cost of our algorithm and demonstrate it by simulation. The experiment results show that our algorithm can save network cost efficiently by comparing to original Bloomjoin algorithm in most scenarios.