Handbook of Massive Data Sets

Pirmais vāks
James Abello, Panos M. Pardalos, Mauricio G.C. Resende
Springer, 2013. gada 21. dec. - 1223 lappuses
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.

No grāmatas satura

Saturs

Algorithmic Aspects of Information Retrieval on the
3
HighPerformance Web Crawling
24
3
47
4
97
String Pattern Matching for a Deluge Survival Kit
151
Searching Large Text Collections
195
Data Compression 245
244
External Memory Data Structures 313
311
Data Warehousing 661
660
Aggregate View Management in Data Warehouses
711
Semistructured Data and XML
743
Overview of High Performance Computers 791
790
The National Scalable Cluster Project
853
Sorting and Selection on Parallel Disk Models
875
Billing in the Large
895
Detecting Fraud in the Real World 911
910

External Memory Algorithms
359
Data Envelopment Analysis DEA in Massive Data Sets
418
Optimization Methods in Massive Data Sets
439
Clustering in Massive Data Sets
501
Managing and Analyzing Massive Data Sets with Data
545
Constructing Summary Data Sets
579
Mining and Monitoring Evolving Data
593
Data Quality in Massive Data Sets
643
Massive Datasets in Astronomy
931
Data Management in Environmental Information Systems
980
Massive Data Sets Issues in Earth Observing
1093
Mining Biomolecular Data Using Background Knowledge
1141
Massive Data Set Issues in Air Pollution Modelling
1169
INDEX
1221
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