Abstract
Monitoring data streams in a distributed system has attracted considerable interest in recent years. The task of feature selection (e.g., by monitoring the information gain of various features) requires a very high communication overhead when addressed using straightforward centralized algorithms. While most of the existing algorithms deal with monitoring simple aggregated values such as frequency of occurrence of stream items, motivated by recent contributions based on geometric ideas we present an alternative approach. The proposed approach enables monitoring values of an arbitrary threshold function over distributed data streams through a set of constraints applied separately on each stream. We report numerical experiments on a real–world data that detect instances where communication between nodes is required, and compare the approach and the results to those recently reported in the literature.
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References
Madden, S., Franklin, M.J.: An architecture for queries over streaming sensor data. In: IEEE Computer Society, ICDE 02, p. 555. Washington, DC, USA (2002)
Dilman, M., Raz, D.: Efficient reactive monitoring. In Proceedings of the Twentieth Annual Joint Conference of the IEEE Computer and Communication Societies, pp. 1012–1019 (2001)
Yi, B.-K., Sidiropoulos, N., Johnson, T., Jagadish, H.V., Faloutsos, C., Biliris, A.: Online datamining for co-evolving time sequences. In: IEEE Computer Society, ICDE 00, p. 13. Washington, USA (2000)
Zhu, Y., Shasha, D.: Statestream: statistical monitoring of thousands of data streamsin real time. In: Very Large Data Base Endowment, pp. 358–369. (2002)
Manjhi, A., Shkapenyuk, V., Dhamdhere, K., Olston, C.: Finding (recently) frequent items in distributed data streams. In: IEEE Computer Society, ICDE 05, pp. 767–778. Los Alamitos, CA, USA (2005)
Sharfman, I., Schuster, A., Keren, D.: A geometric approach to monitoring threshold functions over distributed data streams. In: May, M., Saitta, L. (eds.) Ubiquitous Knowledge Discovery, pp. 163–186. Springer, New York (2010)
Sharfman, I., Schuster, A., Keren, D.: A geometric approach to monitoring threshold functions over distributed data streams. ACM Trans. Database Syst. 32(4), 23:1–23:29 (2007)
Rockafellar, R.T.: Convex Analysis. Princeton University Press, Princeton (1970)
Acknowledgments
The author thanks Danny Keren for bringing the problem to his attention.
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© 2014 Springer-Verlag Berlin Heidelberg
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Kogan, J. (2014). Feature Selection Over Distributed Data Streams. In: Yada, K. (eds) Data Mining for Service. Studies in Big Data, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45252-9_2
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DOI: https://doi.org/10.1007/978-3-642-45252-9_2
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