Frontiers in Massive Data Analysis
2013

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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 scale—terabytes and petabytes—is 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 knowledge—from computer science, statistics, machine learning, and application disciplines—that must be brought to bear to make useful inferences from massive data.

Publication information

190 pages · 6 x 9 · paperback
ISBN Paperback: 0-309-28778-2
ISBN Ebook: 0-309-28779-0
DOI: https://doi.org/10.17226/18374

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National Research Council. 2013. Frontiers in Massive Data Analysis. Washington, DC: The National Academies Press.

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Front Matter i-xiv
Summary 1-10
1 Introduction 11-21
2 Massive Data in Science, Technology, Commerce, National Defense, Telecommunications, and Other Endeavors 22-40
3 Scaling the Infrastructure for Data Management 41-57
4 Temporal Data and Real-Time Algorithms 58-65
5 Large-Scale Data Representations 66-81
6 Resources, Trade-offs, and Limitations 82-92
7 Building Models from Massive Data 93-119
8 Sampling and Massive Data 120-132
9 Human Interaction with Data 133-145
10 The Seven Computational Giants of Massive Data Analysis 146-160
11 Conclusions 161-166
Appendixes 167-168
Appendix A: Acronyms 169-170
Appendix B: Biographical Sketches of Committee Members 171-176

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Frontiers in Massive Data Analysis (2013)

Chapter: Appendixes

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Frontiers in Massive Data Analysis (2013)

Chapter: Appendixes