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.
National Research Council. 2013. Frontiers in Massive Data Analysis. Washington, DC: The National Academies Press. https://doi.org/10.17226/18374.
|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|
|Appendix A: Acronyms||169-170|
|Appendix B: Biographical Sketches of Committee Members||171-176|
The Chapter Skim search tool presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter. You may select key terms to highlight them within pages of each chapter.
The National Academies Press (NAP) has partnered with Copyright Clearance Center's Rightslink service to offer you a variety of options for reusing NAP content. Through Rightslink, you may request permission to reprint NAP content in another publication, course pack, secure website, or other media. Rightslink allows you to instantly obtain permission, pay related fees, and print a license directly from the NAP website. The complete terms and conditions of your reuse license can be found in the license agreement that will be made available to you during the online order process. To request permission through Rightslink you are required to create an account by filling out a simple online form. The following list describes license reuses offered by the National Academies Press (NAP) through Rightslink:
Click here to obtain permission for the above reuses. If you have questions or comments concerning the Rightslink service, please contact:
Rightslink Customer Care
Tel (toll free): 877/622-5543
To request permission to distribute a PDF, please contact our Customer Service Department at 800-624-6242 for pricing.
To request permission to translate a book published by the National Academies Press or its imprint, the Joseph Henry Press, pleaseclick here to view more information.