Partitioning functions for stateful data parallelism in stream processing

Author:

Gedik Buğra

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems

Reference30 articles.

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2. Arasu, A., Manku, G.S.: Approximate counts and quantiles over sliding windows. In: Proceedings of the Symposium on Principles of Database Systems (ACM PODS) (2004)

3. Arasu, A., Babcock, B., Babu, S., Datar, M., Ito, K., Motwani, R., Nishizawa, I., Srivastava, U., Thomas, D., Varma, R., Widom, J.: STREAM: the stanford stream data manager. IEEE Data Eng. Bull. 26(1), 665 (2003)

4. Balkesen, C., Tatbul, N.: Scalable data partitioning techniques for parallel sliding window processing over data streams. In: International Workshop on Data Management for Sensor Networks (DMSN) (2011)

5. Cormode, G., Garofalakis, M., Haas, P., Jermaine, C.: Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches. Now Publishing, Foundations and Trends in Databases Series (2011)

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