Detecting the presence of nodes in MANETs
Proceedings of the second ACM workshop on Challenged networks
Lightweight detection of node presence in MANETs
Ad Hoc Networks
Improved approximate detection of duplicates for data streams over sliding windows
Journal of Computer Science and Technology
Dynamically Maintaining Duplicate-Insensitive and Time-Decayed Sum Using Time-Decaying Bloom Filter
ICA3PP '09 Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing
On-demand time-decaying bloom filters for telemarketer detection
ACM SIGCOMM Computer Communication Review
Collaborative ranking and profiling: exploiting the wisdom of crowds in tailored web search
DAIS'10 Proceedings of the 10th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
Slead: low-memory, steady distributed systems slicing
DAIS'12 Proceedings of the 12th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
Duplicate detection in pay-per-click streams using temporal stateful Bloom filters
International Journal of Data Analysis Techniques and Strategies
Inferential time-decaying Bloom filters
Proceedings of the 16th International Conference on Extending Database Technology
An efficient hybrid approach to per-flow state tracking for high-speed networks
Computer Communications
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Bloom Filters are space-efficient data structures for membership queries over sets. To enable queries for multiplicities of multi-sets, the bitmap in a Bloom Filter as replaced by an array of counters whose values increment on each occurrence. In a data stream model, however, data items arrive at varying rates and recent occurrences are often regarded as more significant than past ones. In most data stream applications, it is critical to handle this "time-sensitivity". Furthermore, data streams with skewed distributions are common in many emerging applications, e.g., traffic engineering and billing, intrusion detection, truding surveillance and outlier detection. For such applications, it is inefficient to allocate counters of uniform size to all buckets. In this paper, we present Time-decaying Bloom Filters (TBF), a Bloom filter that maintains the frequency count for each item in a data stream, and the value of each counter decays with time. For data streams with highly skewed distributions, we proposed further optimization by allowing dynamically allocating free counters to the "large" items. We performed preliminary experiments to verify the optimization.