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A01=Board on Mathematical Sciences and Their Applications
A01=Committee on Applied and Theoretical Statistics
A01=Committee on the Analysis of Massive Data
A01=Division on Engineering and Physical Sciences
A01=National Research Council
Age Group_Uncategorized
Age Group_Uncategorized
Author_Board on Mathematical Sciences and Their Applications
Author_Committee on Applied and Theoretical Statistics
Author_Committee on the Analysis of Massive Data
Author_Division on Engineering and Physical Sciences
Author_National Research Council
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Category1=Non-Fiction
Category=PBT
Category=UNC
Category=UNF
COP=United States
Delivery_Delivery within 10-20 working days
Language_English
PA=Available
Price_€20 to €50
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Frontiers in Massive Data Analysis

Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data.

Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scaleterabytes and petabytesis increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledgefrom computer science, statistics, machine learning, and application disciplinesthat must be brought to bear to make useful inferences from massive data.

Table of Contents
  • Front Matter
  • Summary
  • 1 Introduction
  • 2 Massive Data in Science, Technology, Commerce, National Defense, Telecommunications, and Other Endeavors
  • 3 Scaling the Infrastructure for Data Management
  • 4 Temporal Data and Real-Time Algorithms
  • 5 Large-Scale Data Representations
  • 6 Resources, Trade-offs, and Limitations
  • 7 Building Models from Massive Data
  • 8 Sampling and Massive Data
  • 9 Human Interaction with Data
  • 10 The Seven Computational Giants of Massive Data Analysis
  • 11 Conclusions
  • Appendixes
  • Appendix A: Acronyms
  • Appendix B: Biographical Sketches of Committee Members
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A01=Board on Mathematical Sciences and Their ApplicationsA01=Committee on Applied and Theoretical StatisticsA01=Committee on the Analysis of Massive DataA01=Division on Engineering and Physical SciencesA01=National Research CouncilAge Group_UncategorizedAuthor_Board on Mathematical Sciences and Their ApplicationsAuthor_Committee on Applied and Theoretical StatisticsAuthor_Committee on the Analysis of Massive DataAuthor_Division on Engineering and Physical SciencesAuthor_National Research Councilautomatic-updateCategory1=Non-FictionCategory=PBTCategory=UNCCategory=UNFCOP=United StatesDelivery_Delivery within 10-20 working daysLanguage_EnglishPA=AvailablePrice_€20 to €50PS=Activesoftlaunch
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Product Details
  • Dimensions: 152 x 229mm
  • Publication Date: 03 Oct 2013
  • Publisher: National Academies Press
  • Publication City/Country: United States
  • Language: English
  • ISBN13: 9780309287784

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