Handbook of Massive Data Sets

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

• ISBN13: 9781402004896
• ISBN10: 1402004893
• Format: Hardcover
• Copyright: 2002-04-01
• Publisher: Springer-Verlag New York Inc

Summary

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. 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.

Table of Contents

Preface	xi
PART I. INTERNET AND THE WORLD WIDE WEB
Algorithmic Aspects of Information Retrieval on the Web	3	(22)
A. Broder
M. Henzinger
High-Performance Web Crawling	25	(22)
M. Najork
A. Heydon
Internet Growth: Is There a ``Moore's Law'' for Data Traffic?	47	(50)
K. G. Coffman
A. M. Odlyzko
PART II. MASSIVE GRAPHS
Random Evolution in Massive Graphs	97	(26)
W Aiello
F. Chung
L. Lu
Property Testing in Massive Graphs	123	(28)
O. Goldreich
PART III. STRING PROCESSING AND DATA COMPRESSION
String Pattern Matching for a Deluge Survival Kit	151	(44)
A. Apostolico
M. Crochemore
Searching Large Text Collections	195	(50)
R. Baeza-Yates
A. Moffat
G. Navarro
Data Compression	245	(68)
D. Salomon
PART IV. EXTERNAL MEMORY ALGORITHMS AND DATA STRUCTURES
External Memory Data Structures	313	(46)
L. Arge
External Memory Algorithms	359	(60)
J. S. Vitter
PART V. OPTIMIZATION
Data Envelopment Analysis (DEA) in Massive Data Sets	419	(20)
J. H. Dula
F. J. Lopez
Optimization Methods in Massive Data Sets	439	(34)
P. S. Bradley
O. L. Mangasarian
D. R. Musicant
Wavelets and Multiscale 'Transforms in Astronomical Image Processing	473	(28)
F. Murtagh
J.L. Starck
Clustering in Massive Data Sets	501	(46)
F. Murtagh
PART VI. DATA MANAGEMENT
Managing and Analyzing Massive Data Sets with Data Cubes	547	(32)
M. Riedewald
D. Agrawal
A. El Abbadi
Data Squashing: Constructing Summary Data Sets	579	(14)
W. DuMouchel
Mining and Monitoring Evolving Data	593	(50)
V. Ganti
R. Ramakrishnan
Data Quality in Massive Data Sets	643	(18)
M. F. Goodchild
K. C. Clarke
Data Warehousing	661	(50)
T. Johnson
Aggregate View Management in Data Warehouses	711	(32)
Y. Kotidis
Semistructured Data and XML	743	(48)
D. Suciu
PART VII. ARCHITECTURE ISSUES
Overview of High Performance Computers	791	(62)
A. J. van der Steen
J. Dongarra
The National Scalable Cluster Project	853	(22)
R. Grossman
R. Hollebeek
Sorting and Selection on Parallel Disk Models	875	(20)
S. Rajasekaran
PART VIII. APPLICATIONS
Billing in the Large	895	(16)
A. Hume
Detecting Fraud in the Real World	911	(20)
M. H. Cahill
D. Lambert
J. C. Pinheiro
D. X. Sun
Massive Datasets in Astronomy	931	(50)
R. J. Brunner
S. G. Djorgovski
T. A. Prince
A. S. Szalay
Data Management in Environmental Information Systems	981	(112)
O. Gunther
Massive Data Sets Issues in Earth Observing	1093	(48)
R. Yang
M. Kafatos
Mining Biomolecular Data Using Background Knowledge and Artificial Neural Networks	1141	(28)
Q. Ma
J. T. L. Wang
J. R. Gattiker
Massive Data Set Issues in Air Pollution Modelling	1169	(52)
Z. Zlatev
Index	1221