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Streaming Methods in Data Analysis

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Data Science (BICOD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9147))

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Abstract

A fundamental challenge in processing the massive quantities of information generated by modern applications is in extracting suitable representations of the data that can be stored, manipulated and interrogated on a single machine. A promising approach is in the design and analysis of compact summaries: data structures which capture key features of the data, and which can be created effectively over distributed, streaming data. Popular summary structures include the count distinct algorithms, which compactly approximate item set cardinalities, and sketches which allow vector norms and products to be estimated. These are very attractive, since they can be computed in parallel and combined to yield a single, compact summary of the data. This talk introduces the concepts and examples of compact summaries.

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Acknowledgments

This work supported in part by a Royal Society Wolfson Research Merit Award, funding from the Yahoo Research Faculty Research and Engagement Program, and European Research Council (ERC) Consolidator Grant ERC-CoG-2014-647557.

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Authors and Affiliations

  1. University of Warwick, Coventry, UK

    Graham Cormode

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  1. Graham Cormode
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Correspondence to Graham Cormode .

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Editors and Affiliations

  1. University of Edinburgh, Edinburgh, United Kingdom

    Sebastian Maneth

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© 2015 Springer International Publishing Switzerland

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Cormode, G. (2015). Streaming Methods in Data Analysis. In: Maneth, S. (eds) Data Science. BICOD 2015. Lecture Notes in Computer Science(), vol 9147. Springer, Cham. https://doi.org/10.1007/978-3-319-20424-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-20424-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20423-9

  • Online ISBN: 978-3-319-20424-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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