Handbook of Massive Data Sets

  • James Abello
  • Panos M. Pardalos
  • Mauricio G. C. Resende

Part of the Massive Computing book series (MACO, volume 4)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Internet and the World Wide Web

    1. Front Matter
      Pages 1-1
    2. Andrei Broder, Monika Henzinger
      Pages 3-23
    3. Marc Najork, Allan Heydon
      Pages 25-45
  3. Massive Graphs

    1. Front Matter
      Pages 95-95
    2. William Aiello, Fan Chung, Linyuan Lu
      Pages 97-122
    3. Oded Goldreich
      Pages 123-147
  4. String Processing and Data Compression

    1. Front Matter
      Pages 149-149
    2. Alberto Apostolico, Maxime Crochemore
      Pages 151-194
    3. Ricardo Baeza-Yates, Alistair Moffat, Gonzalo Navarro
      Pages 195-243
    4. David Salomon
      Pages 245-309
  5. External Memory Algorithms and Data Structures

    1. Front Matter
      Pages 311-311
    2. Lars Arge
      Pages 313-357
    3. Jeffrey Scott Vitter
      Pages 359-416
  6. Optimization

    1. Front Matter
      Pages 417-417
    2. José H. Dulá, Francisco J. López
      Pages 419-437
    3. P. S. Bradley, O. L. Mangasarian, D. R. Musicant
      Pages 439-471
    4. Fionn Murtagh, Jean-Luc Starck
      Pages 473-500
    5. Fionn Murtagh
      Pages 501-543

About this book

Introduction

The proliferation of massive data sets brings with it a series of special computational challenges. This "data avalanche" arises in a wide range of scientific and commercial applications. With advances in computer and information technologies, many of these challenges are beginning to be addressed by diverse inter-disciplinary groups, that indude computer scientists, mathematicians, statisticians and engineers, working in dose cooperation with application domain experts. High profile applications indude astrophysics, bio-technology, demographics, finance, geographi­ cal information systems, government, medicine, telecommunications, the environment and the internet. John R. Tucker of the Board on Mathe­ matical Seiences has stated: "My interest in this problern (Massive Data Sets) isthat I see it as the rnost irnportant cross-cutting problern for the rnathernatical sciences in practical problern solving for the next decade, because it is so pervasive. " The Handbook of Massive Data Sets is comprised of articles writ­ ten by experts on selected topics that deal with some major aspect of massive data sets. It contains chapters on information retrieval both in the internet and in the traditional sense, web crawlers, massive graphs, string processing, data compression, dustering methods, wavelets, op­ timization, external memory algorithms and data structures, the US national duster project, high performance computing, data warehouses, data cubes, semi-structured data, data squashing, data quality, billing in the large, fraud detection, and data processing in astrophysics, air pollution, biomolecular data, earth observation and the environment.

Keywords

Web Crawling algorithms data compression data warehouse image processing information information system optimization performance

Editors and affiliations

  • James Abello
    • 1
  • Panos M. Pardalos
    • 2
  • Mauricio G. C. Resende
    • 1
  1. 1.AT&T Labs ResearchUSA
  2. 2.University of FloridaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-0005-6
  • Copyright Information Springer-Verlag US 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-4882-5
  • Online ISBN 978-1-4615-0005-6
  • Series Print ISSN 1569-2698
  • Series Online ISSN 2468-8738
  • About this book