Handbook of Massive Data Sets

Abello, James; Pardalos, Panos M.; Resende, Mauricio G.C.

ISBN 10: 1402004893 / ISBN 13: 9781402004896

Edité par Springer, 2002

A propos de cet article

Clean, solid hardcover. Owner name blacked out on top page edge and owner plate inside front cover, else no other marks inside. Light rub to spine ends and corners. 2002 printing. International and priority sales will require extra shipping due to size and weight.

N° de réf. du vendeur 040674

Détails bibliographiques

• Titre: Handbook of Massive Data Sets
• Éditeur: Springer
• Date de parution: 2002
• Langue: anglais
• ISBN à 10 chiffres: 1402004893
• ISBN à 13 chiffres: 9781402004896
• Reliure: Hardcover
• État de l'article: Very Good

A propos de ce titre

Synopsis

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.