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Mit Lincoln Laboratory Ser.: Mathematics of Big Data : Spreadsheets, Databases, Matrices, and Graphs by Jeremy Kepner and Hayden Jananthan (2018, Hardcover)
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MATHEMATICS OF BIG DATA: SPREADSHEETS, DATABASES, MATRICES, AND GRAPHS (MIT LINCOLN LABORATORY SERIES) By Jeremy Kepner & Hayden Jananthan - Hardcover **BRAND NEW**.
- Buy it nowMATHEMATICS OF BIG DATA: SPREADSHEETS, DATABASES, By Jeremy Kepner & NEW
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About this product
Product Identifiers
PublisherMIT Press
ISBN-100262038390
ISBN-139780262038393
eBay Product ID (ePID)243128698
Product Key Features
Number of Pages448 Pages
LanguageEnglish
Publication NameMathematics of Big Data : Spreadsheets, Databases, Matrices, and Graphs
SubjectComputer Science, General, Databases / Data Mining
Publication Year2018
TypeTextbook
Subject AreaMathematics, Computers
AuthorJeremy Kepner, Hayden Jananthan
SeriesMit Lincoln Laboratory Ser.
FormatHardcover
Dimensions
Item Height1.2 in
Item Weight35.3 Oz
Item Length9.4 in
Item Width7.3 in
Additional Product Features
Intended AudienceTrade
LCCN2017-057054
IllustratedYes
SynopsisThe 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., 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.
LC Classification NumberQA76.9.B45K47 2018
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