Riassunto
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.
Contenuti
Preface. Part I: Internet and the World Wide Web. 1. Algorithmic Aspects of Information Retrieval on the Web; A. Broder, M. Henzinger. 2. High-Performance Web Crawling; M. Najork, A. Heydon. 3. Internet Growth: Is There a `Moore's Law' for Data Traffic? K.G. Coffman, A.M. Odlyzko. Part II: Massive Graphs. 4. Random Evolution in Massive Graphs; W. Aiello, et al. 5. Property Testing in Massive Graphs; O. Goldreich. Part III: String Processing and Data Compression. 6. String Pattern Matching for a Deluge Survival Kit; A. Apostolico, M. Crochemore. 7. Searching Large Text Collections; R. Baeza-Yates, et al. 8. Data Compression; D. Salomon. Part IV: External Memory Algorithms and Data Structures. 9. External Memory Data Structures; L. Arge. 10. External Memory Algorithms; J.S. Vitter. Part V: Optimization. 11. Data Envelopment Analysis (DEA) in Massive Data Sets; J.H. Dulá, F.J. López. 12. Optimization Methods in Massive Data Sets; P.S. Bradley, et al. 13. Wavelets and Multiscale Transforms in Astronomical Image Processing; F. Murtagh, J.L. Starck. 14. Clustering in Massive Data Sets; F. Murtagh. Part VI: Data Management. 15. Managing and Analyzing Massive Data Sets with Data Cubes; M. Riedewald, et al. 16. Data Squashing: Constructing Summary Data Sets; W. DuMouchel. 17. Mining and Monitoring Evolving Data; V. Ganti, R. Ramakrishnan. 18. Data Quality in Massive Data Sets; M.F. Goodchild, K.C. Clarke. 19. Data Warehousing; T. Johnson. 20. Aggregate View Management in Data Warehouses; Y. Kotidis. 21. Semistructured Data and XML; D. Suciu. Part VII: Architecture Issues. 22. Overview of High Performance Computers; A.J. van der Steen, J. Dongarra. 23. The National Scalable Cluster Project; R. Grossman, R. Hollebeek. 24. Sorting and Selection on Parallel Disk Models; S. Rajasekaran. Part VIII: Applications. 25. Billing in the Large; A. Hume. 26. Detecting Fraud in the Real World; M.H. Cahill, et al. 27. Massive Datasets in Astronomy; R.J. Brunner, et al. 28. Data Management in Environmental Information Systems; O. Günther. 29. Massive Data Sets Issues in Earth Observing; R. Yang, M. Kafatos. 30. Mining Biomolecular Data Using Background Knowledge and Artificial Neural Networks; Q. Ma, et al. 31. Massive Data Set Issues in Air Pollution Modelling; Z. Zlatev. Index.
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