Cardinality estimation and dynamic length adaptation for Bloom filters
Distributed and Parallel Databases
A Generalized Bloom Filter to Secure Distributed Network Applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
Understanding bloom filter intersection for lazy address-set disambiguation
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
One is enough: distributed filtering for duplicate elimination
Proceedings of the 20th ACM international conference on Information and knowledge management
International Journal of Bio-Inspired Computation
TBF: a high-efficient query mechanism in de-duplication backup system
GPC'12 Proceedings of the 7th international conference on Advances in Grid and Pervasive Computing
Distance-aware bloom filters: Enabling collaborative search for efficient resource discovery
Future Generation Computer Systems
Toward intersection filter-based optimization for joins in MapReduce
Proceedings of the 2nd International Workshop on Cloud Intelligence
Sea depth measurement with restricted floating sensors
ACM Transactions on Embedded Computing Systems (TECS)
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A Bloom filter is an effective, space-efficient data structure for concisely representing a set, and supporting approximate membership queries. Traditionally, the Bloom filter and its variants just focus on how to represent a static set and decrease the false positive probability to a sufficiently low level. By investigating mainstream applications based on the Bloom filter, we reveal that dynamic data sets are more common and important than static sets. However, existing variants of the Bloom filter cannot support dynamic data sets well. To address this issue, we propose dynamic Bloom filters to represent dynamic sets, as well as static sets and design necessary item insertion, membership query, item deletion, and filter union algorithms. The dynamic Bloom filter can control the false positive probability at a low level by expanding its capacity as the set cardinality increases. Through comprehensive mathematical analysis, we show that the dynamic Bloom filter uses less expected memory than the Bloom filter when representing dynamic sets with an upper bound on set cardinality, and also that the dynamic Bloom filter is more stable than the Bloom filter due to infrequent reconstruction when addressing dynamic sets without an upper bound on set cardinality. Moreover, the analysis results hold in stand-alone applications, as well as distributed applications.