Abstract
Many applications require the discovery of items which have occur frequently within multiple distributed data streams. Past solutions for this problem either require a high degree of error tolerance or can only provide results periodically. In this paper we introduce a new algorithm designed for continuously tracking frequent items over distributed data streams providing either exact or approximate answers. We tested the efficiency of our method using two real-world data sets. The results indicated significant reduction in communication cost when compared to naïve approaches and an existing efficient algorithm called Top-K Monitoring. Since our method does not rely upon approximations to reduce communication overhead and is explicitly designed for tracking frequent items, our method also shows increased quality in its tracking results.
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Fuller, R., Kantardzic, M. (2007). FIDS: Monitoring Frequent Items over Distributed Data Streams. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_35
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DOI: https://doi.org/10.1007/978-3-540-73499-4_35
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