Handbook of Massive Data SetsJames Abello, Panos M. Pardalos, Mauricio G.C. Resende Springer Science & Business Media, 2002. gada 31. marts - 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. |
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Saturs
Algorithmic Aspects of Information Retrieval on the Web | 3 |
HighPerformance Web Crawling | 25 |
Internet Growth Is There a Moores Law For Data Traffic? | 47 |
MASSIVE GRAPHS | 95 |
Random Evolution in Massive Graphs | 97 |
Property Testing in Massive Graphs | 123 |
STRING PROCESSING AND DATA COMPRESSION | 149 |
String Pattern Matching for a Deluge Survival Kit | 151 |
Data Squashing Constructing Summary Data Sets | 579 |
Mining and Monitoring Evolving Data | 593 |
Data Quality in Massive Data Sets | 643 |
Data Warehousing | 661 |
Aggregate View Management in Data Warehouses | 711 |
Semistructured Data and XML | 743 |
ARCHITECTURE ISSUES | 789 |
Overview of High Performance Computers | 791 |
Searching Large Text Collections | 195 |
Data Compression | 245 |
EXTERNAL MEMORY ALGORITHMS AND DATA STRUCTURES | 311 |
External Memory Data Structures | 313 |
External Memory Algorithms | 359 |
OPTIMIZATION | 417 |
Data Envelopment Analysis DEA in Massive Data Sets | 419 |
Optimization Methods in Massive Data Sets | 439 |
Wavelets and Mutiscale Transforms in Astronomical Image Processing | 473 |
Clustering in Massive Data Sets | 501 |
DATA MANAGEMENT | 545 |
Managing and Analyzing Massive Data Sets with Data Cubes | 547 |
The National Scalable Cluster Project | 851 |
Sorting and Selection on Parallel Disk Models | 873 |
APPLICATIONS | 891 |
Billing in the Large | 893 |
Detecting Fraud in the Real World | 909 |
Massive Datasets in Astronomy | 929 |
Data Management in Environmental information Systems | 979 |
Massive Data Sets Issues in Earth Observing | 1091 |
Mining Biomolecular Data Using Background Knowledge and Artificial Neural Networks | 1139 |
Massive Data Set Issues in Air Pollution Modelling | 1167 |
INDEX | 1218 |
Citi izdevumi - Skatīt visu
Handbook of Massive Data Sets James Abello,Panos M. Pardalos,Mauricio G.C. Resende Ierobežota priekšskatīšana - 2013 |
Handbook of Massive Data Sets James Abello,Panos M. Pardalos,Mauricio G.C. Resende Priekšskatījums nav pieejams - 2013 |
Bieži izmantoti vārdi un frāzes
aggregate algorithm analysis applications approximately attributes B-tree bandwidth block bound cache clusters components compression Computer Science Conf data cube data mining data structures data warehouse database decision tree decoder defined deleted dimensions discussed disk distribution document efficient elements encoder example Figure fraud function GB/s Gflop/s graph Huffman coding I/O bounds IEEE input insertion internal memory Internet J. S. Vitter linear linear program machines Massive Data Sets matching Mb/s merge method multidimensional Napster node objects OLAP operations optimal parallel parameters partition Peak performance pixels points power law prefix sum problem Proc Proceedings processors query random result retrieval sample selection server SIGMOD sorting space storage stored string suffix array symbol techniques Terabytes Theoretical tion traffic transform tree tuples update values variables vector wavelet
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