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|Title:||Approximately detecting duplicates for probabilistic data streams over sliding windows|
|Citation:||Proceedings of the 3rd International Symposium and Parallel Architectures, Algorithm and Programming, 2010 / Xianchao Zhang, Wenxin Liang, Hong Shen, and Guoliang Chen (eds.): pp.263-267|
|Conference Name:||International Symposium on Parallel Architectures, Algorithm and Programming (3rd : 2010 : Dalian, China)|
|Xiujun Wang, Hong Shen|
|Abstract:||Proceedings of the 3rd International Symposium and Parallel Architectures, Algorithm and Programming, Xianchao Zhang, Wenxin Liang, Hong Shen, and Guoliang Chen (eds.): pp.63-267Abstract-A probabilistic data stream S is defined as a sequence of uncertain tuples <;ti, pi >;, i = 1...∞, with the semantics that element ti occurs in the stream with probability pi ϵ (0, 1). Thus each distinct element t, which occurs in tuples of S, has an existential probability based on the tuples: <; ti = t, pi >; ϵ S. Existing duplicate detection methods for a traditional deterministic data stream can't maintain these existential probabilities for elements in S, which is important query information. In this paper, we present a novel data structure, Floating Counter Bloom Filter (FCBF), as an extension of CBF, which can maintain these existential probabilities effectively. Based on FCBF, we present an efficient algorithm to approximately detect duplicates for probabilistic data streams over sliding windows. Given a sliding window size W and floating counter number N, for any t which occurs in the past sliding window, our method outputs the accurate existential probability of t with probability 1-(1/2)ln(2)*N/W. Our experimental results on the synthetic data verify the effectiveness of our approach.|
|Keywords:||Counting Bloom Filter|
Floating Counter Bloom Filter
Probabilistic Data Stream
|Rights:||© 2010 IEEE|
|Appears in Collections:||Aurora harvest|
Computer Science publications
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