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.
Table of Contents |
skim chapter | |
|---|---|---|
| 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 | |
Scott Weidman, director of the Board on Mathematical Science and their Applications at the NRC, explains the charge and key recommendation of the report along with the challenges and opportunties the Massive Data presents.
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