Mathematics of Big Data - Spreadsheets, Databases, Matrices, and Graphs

Kepner, Jeremy Jeremy Kepner, Hayden Jananthan, Charles E. Leiserson, James Ward,

ISBN 10: 0262038390 ISBN 13: 9780262038393
Editore: MIT Press, 2018
Nuovi hardcover

Da Chiron Media, Wallingford, Regno Unito Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Venditore AbeBooks dal 2 agosto 2010

Questo articolo specifico non è più disponibile.

Riguardo questo articolo

Descrizione:

Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. Codice articolo 6666-GRD-9780262038393

Segnala questo articolo

Riassunto:

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.

Informazioni sull'autore: 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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Dati bibliografici

Titolo: Mathematics of Big Data - Spreadsheets, ...
Casa editrice: MIT Press
Data di pubblicazione: 2018
Legatura: hardcover
Condizione: New

I migliori risultati di ricerca su AbeBooks

Vedi altre 11 copie di questo libro

Vedi tutti i risultati per questo libro