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Weighted Random Sampling over Data Streams

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9295))

Abstract

In this work, we present a comprehensive treatment of weighted random sampling (WRS) over data streams. More precisely, we examine two natural interpretations of the item weights, describe an existing algorithm for each case [3, 8], discuss sampling with and without replacement and show adaptations of the algorithms for several WRS problems and evolving data streams.

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Notes

  1. 1.

    We say “supposed” because even though WRS is best described with a sequential sampling procedure, it is not inherently sequential. Algorithm A-ES [8] which we will use to solve WRS-W problems can be executed on sequential, parallel and distributed settings.

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Acknowledgments

The present work was supported in part by the project ATLAS (Advanced Tourism Planning), GSRT/CO-OPERATION/11SYN-10-1730, and by national ETAA funds.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece

    Pavlos S. Efraimidis

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  1. Pavlos S. Efraimidis
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Correspondence to Pavlos S. Efraimidis .

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Editors and Affiliations

  1. University of Patras, Patras, Greece

    Christos Zaroliagis

  2. Technological Educational Institute of Athens, Athens, Greece

    Grammati Pantziou

  3. University of Ioannina, Ioannina, Greece

    Spyros Kontogiannis

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Efraimidis, P.S. (2015). Weighted Random Sampling over Data Streams. In: Zaroliagis, C., Pantziou, G., Kontogiannis, S. (eds) Algorithms, Probability, Networks, and Games. Lecture Notes in Computer Science(), vol 9295. Springer, Cham. https://doi.org/10.1007/978-3-319-24024-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-24024-4_12

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  • Online ISBN: 978-3-319-24024-4

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