An algorithm for approximate membership checking with application to password security
Information Processing Letters
Summary cache: a scalable wide-area web cache sharing protocol
IEEE/ACM Transactions on Networking (TON)
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
Compression and Coding Algorithms
Compression and Coding Algorithms
SIAM Journal on Computing
An optimal Bloom filter replacement
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Less hashing, same performance: building a better bloom filter
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Compact Hash Tables Using Bidirectional Linear Probing
IEEE Transactions on Computers
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Real-time memory efficient data redundancy removal algorithm
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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SPIN'10 Proceedings of the 17th international SPIN conference on Model checking software
High throughput data redundancy removal algorithm with scalable performance
Proceedings of the 6th International Conference on High Performance and Embedded Architectures and Compilers
Real-time approximate Range Motif discovery & data redundancy removal algorithm
Proceedings of the 14th International Conference on Extending Database Technology
Proceedings of the 15th International Conference on Extending Database Technology
Streaming quotient filter: a near optimal approximate duplicate detection approach for data streams
Proceedings of the VLDB Endowment
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A Bloom filter is a very compact data structure that supports approximate membership queries on a set, allowing false positives. We propose several new variants of Bloom filters and replacements with similar functionality. All of them have a better cache-efficiency and need less hash bits than regular Bloom filters. Some use SIMD functionality, while the others provide an even better space efficiency. As a consequence, we get a more flexible trade-off between false-positive rate, space-efficiency, cache-efficiency, hash-efficiency, and computational effort. We analyze the efficiency of Bloom filters and the proposed replacements in detail, in terms of the false-positive rate, the number of expected cache-misses, and the number of required hash bits. We also describe and experimentally evaluate the performance of highly tuned implementations. For many settings, our alternatives perform better than the methods proposed so far.