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Massive Data Sets Proceedings of a Workshop (1996) / Chapter Skim
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Statistics and Massive Data Sets: one View from the Social Sciences
Pages 33-38

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 33...
... Toc3~7 Lois T>erform~nr~ ~mrl~-ct~t;^nc ;nt~c~=t=A ., ~, ~ -1~ ~,J~ ^~ ~ ~ vv~1~uc~ul~llo 1llc=,~ ac~£::~t within local and external networks have brought unprecedented computing ponder to the desktop along with interactive graphics, mass storage capabilities, and fingertip access to a world of computing and data resources. Each transition has opened new opportunities for those who work with demographic, social, economic behavioral, and health data to manage larger quantities of data and to apply more sophisticated techniques to the analysis of those data.
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From page 34...
... social scientists' and others who work with census and survey data, are often faced with the necessity of working with ciata sets of such magnitude and complexity that the human and technological capabilities required to make effective use of the cia,ta are stretched to their limits-and often beyond. Even today, researchers may find it necessa.r.~to coordinate three or more layers of support personnel to assist them with their efforts to retrieve information from data sets ranging to gigabytes (GB)
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From page 35...
... equally- well to the social sciences. ~ _ _1 ' _ ~ ~ea~ca~ecT parallel and multiple processing systems have the potential to essentially- eliminate the I/O and processing bottlenecks typically associated with handling files containing millions of data records.
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From page 36...
... tabuiations may be readily generated as well as summary statistics on one item within the cross-categories of others. Some statistical packages provide modes of operation that are interactive or that allow the user to chain processes in ways that, in effect can give intera,ctive access to Satan but not to data sets the size of the PUMS and not with the speect a,nc]
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From page 37...
... and hardware architecture on the host hardware. NIore simply' the active data are stored in RAl\I while they- are in use and lien, portions of the program code are designed to be held in tile on-chip instruction caches of the processors throughout the computing intensive portions of system execution.
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Key Terms

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  • mass storage
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  • ciesin facilities
  • college attendance
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  • expertise required
  • data resources
  • multiple processing
  • college enrollment
  • massive demographic

  • enrollment rates
  • studies center
  • packages provide
  • provide modes
  • effort involving

  • symmetric multiprocessing
  • american housing
  • costly computing
  • statistics mortality
  • nature highly

  • easier access
  • graphics tools
  • significant technical
  • news reporters
  • hardware architecture


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