Frontiers in Massive Data Analysis
English
By (author): Board on Mathematical Sciences and Their Applications Committee on Applied and Theoretical Statistics Committee on the Analysis of Massive Data Division on Engineering and Physical Sciences National Research Council
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