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  3. Small Summaries for Big Data
  • Cited by 36
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    • Publisher:
      Cambridge University Press
      Publication date:
      22 October 2020
      12 November 2020
      ISBN:
      9781108769938
      9781108477444
      DOI:
      https://doi.org/10.1017/9781108769938
      Dimensions:
      (228 x 152 mm)
      Weight & Pages:
      0.51kg, 278 Pages
      Dimensions:
      Weight & Pages:
      • Subjects:
      Algorithmics, Complexity, Computer Algebra, Computational Geometry, Knowledge Management, Databases and Data Mining, Computer Science
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    Subjects:
    Algorithmics, Complexity, Computer Algebra, Computational Geometry, Knowledge Management, Databases and Data Mining, Computer Science
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    Book description

    The massive volume of data generated in modern applications can overwhelm our ability to conveniently transmit, store, and index it. For many scenarios, building a compact summary of a dataset that is vastly smaller enables flexibility and efficiency in a range of queries over the data, in exchange for some approximation. This comprehensive introduction to data summarization, aimed at practitioners and students, showcases the algorithms, their behavior, and the mathematical underpinnings of their operation. The coverage starts with simple sums and approximate counts, building to more advanced probabilistic structures such as the Bloom Filter, distinct value summaries, sketches, and quantile summaries. Summaries are described for specific types of data, such as geometric data, graphs, and vectors and matrices. The authors offer detailed descriptions of and pseudocode for key algorithms that have been incorporated in systems from companies such as Google, Apple, Microsoft, Netflix and Twitter.

    Reviews

    'A very thorough compendium of sketching and streaming algorithms, and an excellent resource for anyone interested in learning about them, understanding how they work and deploying them in applications. Good job!'

    Piotr Indyk - Massachusetts Institute of Technology

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