PCIR: Combining DHTs and peer clusters for efficient full-text P2P indexing
Computer Networks: The International Journal of Computer and Telecommunications Networking
Cardinality estimation and dynamic length adaptation for Bloom filters
Distributed and Parallel Databases
Understanding bloom filter intersection for lazy address-set disambiguation
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
Probabilistic threshold join over distributed uncertain data
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Join processing using Bloom filter in MapReduce
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Toward intersection filter-based optimization for joins in MapReduce
Proceedings of the 2nd International Workshop on Cloud Intelligence
TWINS: Efficient time-windowed in-network joins for sensor networks
Information Sciences: an International Journal
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Bloom filter based algorithms have proven successful as very efficient technique to reduce communication costs of database joins in a distributed setting. However, the full potential of bloom filters has not yet been exploited. Especially in the case of multi-joins, where the data is distributed among several sites, additional optimization opportunities arise, which require new bloom filter operations and computations. In this paper, we present these extensions and point out how they improve the performance of such distributed joins. While the paper focuses on efficient join computation, the described extensions are applicable to a wide range of usages, where bloom filters are facilitated for compressed set representation.