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Mathematics of big data: spreadsheets, databases, matrices, and graphs

Mathematics of big data: spreadsheets, databases, matrices, and graphs
Catalogue Information
Field name Details
Dewey Class 005.7 (DDC 23.)
Title Mathematics of big data (M) : spreadsheets, databases, matrices, and graphs / Jeremy Kepner and Hayden Jananthan ; foreword by Charles E. Leiserson
Author Kepner, Jeremy
Added Personal Name Jananthan, Hayden
Publication Cambridge, Massachusetts : London, England : The MIT Press , 2018
Physical Details xxi, 418 pages : ill. ; 24 cm
Series MIT Lincoln laboratory series
ISBN 9780262038393
Summary Note 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.:
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Item Information
Barcode Shelf Location Collection Volume Ref. Branch Status Due Date
0000000043966 519.1 KEP
General   SISSA . . On Loan, Reserved . 24 Jul 2019
. Catalogue Record 49420 ItemInfo Beginning of record . Catalogue Record 49420 ItemInfo Top of page .


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