new book: Handbook of Massive Data Sets
Date: 4 Jun 2002 12:45:12 -0800
Message-ID: <b0e17b38.0206041024.33ad7e98_at_posting.google.com>
>>>>>>>>>Sorry for multiple postings<<<<<<<<<
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Dear Colleague,
We are pleased to announce a new book, titled
HANDBOOK OF MASSIVE DATA SETS Edited by James Abello, Panos M. Pardalos, and Mauricio G. C. Resende.
Published in May 2002 by Kluwer Academic Publishers, Dordrecht.
Check the link: http://www.research.att.com/~mgcr/hmds.html for table of contents, abstracts, list of contributors, and ordering information.
PREFACE 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 include computer scientists, mathematicians, statisticians and engineers, working in close cooperation with application domain experts. High profile applications include astrophysics, bio-technology, demographics, finance, geographical information systems, government, medicine, telecommunications, the environment and the internet.
John R. Tucker of the Board on Mathematical Sciences has stated:
"My interest in this problem (Massive Data Sets) is that I see it
as the most important cross-cutting problem for the mathematical
sciences in practical problem solving for the next decade, because
it is so pervasive.''
The Handbook of Massive Data Sets is comprised of articles written 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, clustering methods, wavelets, optimization, external memory algorithms and data structures, the US national cluster 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.
We would like to take the opportunity to thank the authors of the chapters, the anonymous referees, AT&T Labs Research, and the Center for Applied Optimization, University of Florida for supporting this effort. The first editor wants to express his appreciation to Dave Belanger for his continued support. Special thanks and appreciation go to Dr. Hong-Xuan Huang for assisting us with LaTeX and other issues in the preparation of the camera-ready copy of this handbook. Finally, we would like to thank the Kluwer Academic Publishers for their assistance.
Handbook of Massive Data Sets
Kluwer Academic Publishers
hmds_at_research.att.com
James Abello (Information Visualization Research Department, AT&T Labs Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJ 07932 USA, abello_at_research.att.com)
Panos M. Pardalos (Center for Applied Optimization, ISE Department, 303 Weil Hall, University of Florida, Gainesville, FL 32605 USA, pardalos_at_ufl.edu)
Mauricio G. C. Resende (Algorithms and Optimization Research Department, AT&T Labs Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJ 07932 USA, mgcr_at_research.att.com)
Editors
Check the link: http://www.research.att.com/~mgcr/hmds.html for table of contents, abstracts, list of contributors, and ordering information. Received on Tue Jun 04 2002 - 22:45:12 CEST