Frontiers in Massive Data Analysis

Committee on the Analysis of Massive Data, Committee on Applied and Theoretical Statistics, Division on Engineering and Physical Sciences, National Research Council, Board on Mathematical Sciences and Their Applications

ISBN 10: 0309287782 / ISBN 13: 9780309287784

Published by National Academies Press, 2013

About this Item

Used book that is in clean, average condition without any missing pages.

Seller Inventory # 39619756-75

Bibliographic Details

• Title: Frontiers in Massive Data Analysis
• Publisher: National Academies Press
• Publication Date: 2013
• Language: English
• ISBN 10: 0309287782
• ISBN 13: 9780309287784
• Binding: Soft cover
• Condition: Good
• Weight: 0.7 pounds

About this title

Synopsis

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.