Handbook of Massive Data SetsJames 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
1.–5. rezultāts no 68.
5. lappuse
... example , probabilistic models ( Maron and Kuhns 1960 , Robertson and Jones 1976 , Bookstein and Swanson 1974 , van Rijsbergen 1979 , Fuhr 1989 ) , and cognitive models ( Salton 1980 , Ellis 1992 ) , which we will not discuss in this ...
... example , probabilistic models ( Maron and Kuhns 1960 , Robertson and Jones 1976 , Bookstein and Swanson 1974 , van Rijsbergen 1979 , Fuhr 1989 ) , and cognitive models ( Salton 1980 , Ellis 1992 ) , which we will not discuss in this ...
6. lappuse
... example , to de- termine what kind of stemming is appropriate the system has to identify the underlying language . This is made difficult by the fact that many Web documents are short and might consist of a mixture of languages . 3 ...
... example , to de- termine what kind of stemming is appropriate the system has to identify the underlying language . This is made difficult by the fact that many Web documents are short and might consist of a mixture of languages . 3 ...
18. lappuse
... example on the number of threads , the number of open connections , etc. ) External load balancing means that a crawler should not overload any server or connection ( See Koster for general guidelines ( Koster 1993 ) . ) This is ...
... example on the number of threads , the number of open connections , etc. ) External load balancing means that a crawler should not overload any server or connection ( See Koster for general guidelines ( Koster 1993 ) . ) This is ...
51. lappuse
... example , the introduction to Gilder ( 1997 ) talks of " the coming world of cheap , unlimited bandwidth . " In the US alone there are now over half a dozen long haul carriers that either have or will have very substantial national ...
... example , the introduction to Gilder ( 1997 ) talks of " the coming world of cheap , unlimited bandwidth . " In the US alone there are now over half a dozen long haul carriers that either have or will have very substantial national ...
53. lappuse
... examples where such statements . are either implausible or even demonstrably incorrect . For example , Keith Mitchell , executive chairman of LINX , the London Internet Ex- change , Ltd. , is quoted in Jander ( 2000 ) as saying in March ...
... examples where such statements . are either implausible or even demonstrably incorrect . For example , Keith Mitchell , executive chairman of LINX , the London Internet Ex- change , Ltd. , is quoted in Jander ( 2000 ) as saying in March ...
Saturs
3 | |
24 | |
47 | |
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 |
Citi izdevumi - Skatīt visu
Handbook of Massive Data Sets James Abello,Panos M. Pardalos,Mauricio G.C. Resende Ierobežota priekšskatīšana - 2002 |
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 complex components compression Computer Science Conf contains data cube data mining data structures data warehouse database decision tree decoder defined deleted dimension 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 matching Mb/s merge method multidimensional Napster node objects OLAP operations optimal parallel parameters partition Peak performance pixels power law prefix sum problem Proc processors query random result retrieval sample selection server SIGMOD sorting space spatial storage stored string suffix array symbol techniques Terabytes Theoretical tion traffic transform tree tuples update values variables vector wavelet