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Mathematics of Big Data

by Jeremy Kepner and Hayden Jananthan

  • Hardcover
    $142.09
ISBN: 9780262038393
ANNOTATION:
The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies.
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OTHER FORMATS:
  • Hardcover
    $142.09
ISBN: 9780262038393
ANNOTATION:
The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies.

Annotation

The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies.

Publisher Description

The first book to present the common mathematical foundations of big data analysis across a range of applications and technologies.

Today, the volume, velocity, and variety of data are increasing rapidly across a range of fields, including Internet search, healthcare, finance, social media, wireless devices, and cybersecurity. Indeed, these data are growing at a rate beyond our capacity to analyze them. The tools-including spreadsheets, databases, matrices, and graphs-developed to address this challenge all reflect the need to store and operate on data as whole sets rather than as individual elements. This book presents the common mathematical foundations of these data sets that apply across many applications and technologies. Associative arrays unify and simplify data, allowing readers to look past the differences among the various tools and leverage their mathematical similarities in order to solve the hardest big data challenges.

The book first introduces the concept of the associative array in practical terms, presents the associative array manipulation system D4M (Dynamic Distributed Dimensional Data Model), and describes the application of associative arrays to graph analysis and machine learning. It provides a mathematically rigorous definition of associative arrays and describes the properties of associative arrays that arise from this definition. Finally, the book shows how concepts of linearity can be extended to encompass associative arrays. Mathematics of Big Data can be used as a textbook or reference by engineers, scientists, mathematicians, computer scientists, and software engineers who analyze big data.

Author Biography

Jeremy Kepner is an MIT Lincoln Laboratory Fellow, Founder and Head of the MIT Lincoln Laboratory Supercomputing Center, and Research Affiliate in MIT's Mathematics Department. Hayden Jananthan is a PhD candidate in the Department of Mathematics at Vanderbilt University. Charles E. Leiserson is Professor of Computer Science and Engineering at the Massachusetts Institute of Technology.

Product Details

Author
Jeremy Kepner, Hayden Jananthan
Pages
448
Publisher
Mit Press Ltd
Year
2018
ISBN-10
0262038390
ISBN-13
9780262038393
Format
Hardcover
Subtitle
Spreadsheets, Databases, Matrices, and Graphs
Country of Publication
United States
Audience Age
18-99
Illustrations
99 figures; 99 Illustrations, unspecified
Series
Mit Lincoln Laboratory Series
Publication Date
2018-07-17
Short Title
Mathematics of Big Data
Language
English
Audience
General/Trade