Practical performance of Bloom filters and parallel free-text searching
Communications of the ACM
Summary cache: a scalable wide-area web cache sharing protocol
IEEE/ACM Transactions on Networking (TON)
A second look at bloom filters
Communications of the ACM
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Longest prefix matching using bloom filters
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Bloom histogram: path selectivity estimation for XML data with updates
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Dynamic adaptive data structures for monitoring data streams
Data & Knowledge Engineering
Finding frequent items in data streams using ESBF
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Enhancing counting bloom filters through Huffman-coded multilayer structures
IEEE/ACM Transactions on Networking (TON)
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Bloom filters are not able to handle deletes and inserts on multisets over time. This is important in many situations when streamed data evolve rapidly and change patterns frequently. Counting Bloom Filters (CBF) have been proposed to overcome this limitation and allow for the dynamic evolution of Bloom filters. The only dynamic approach to a compact and efficient representation of CBF are the Spectral Bloom Filters (SBF).In this paper we propose the Dynamic Count Filters (DCF) as a new dynamic and space-time efficient representation of CBF. Although DCF does not make a compact use of memory, it shows to be faster and more space efficient than any previous proposal. Results show that the proposed data structure is more efficient independently of the incoming data characteristics.