A linear-time probabilistic counting algorithm for database applications
ACM Transactions on Database Systems (TODS)
Managing persistent objects in a multi-level store
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Summary cache: a scalable wide-area web cache sharing protocol
IEEE/ACM Transactions on Networking (TON)
Counting large numbers of events in small registers
Communications of the ACM
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Mid-tier caching: the TimesTen approach
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Middle-tier database caching for e-business
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Middle-Tier Extensible Data Management
World Wide Web
Maintaining time-decaying stream aggregates
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Approximately detecting duplicates for streaming data using stable bloom filters
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
An improved construction for counting bloom filters
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
A Generalized Bloom Filter to Secure Distributed Network Applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
Duplicate detection in pay-per-click streams using temporal stateful Bloom filters
International Journal of Data Analysis Techniques and Strategies
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Distributed enterprise applications are typically based on a multiple–tier client–server architecture where large volume of data is transferred between tiers frequently. When the amount and frequency of data to be transferred become large, network bandwidth will become a bottleneck and efficient middle–tier data management is critical. In this paper, we propose a semi–persistence model to capture the evolving nature of data in a middle tier data management system. We also propose to use Bloom Filters (BF) as an efficient data structure to maintain the time-sensitive frequency profile of the underlying data items. We first extend the standard Bloom Filters by replacing the bit-vector with an array of counters. We then optimize it by allocating lowest space necessary for each counter to store its value. The preliminary experiments show that the optimized BF achieves considerable improvement on space usage while providing the same results of frequency profile.