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Mathematics of Big Data: Spreadsheets, Databases, Matrices, and Graphs by Jeremy
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- ISBN-13
- 9780262038393
- Book Title
- Mathematics of Big Data
- ISBN
- 9780262038393
- Subject Area
- Mathematics, Computers
- Publication Name
- Mathematics of Big Data : Spreadsheets, Databases, Matrices, and Graphs
- Publisher
- MIT Press
- Item Length
- 9.4 in
- Subject
- Computer Science, General, Databases / Data Mining
- Publication Year
- 2018
- Series
- Mit Lincoln Laboratory Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.2 in
- Item Weight
- 35.3 Oz
- Item Width
- 7.3 in
- Number of Pages
- 448 Pages
關於產品
Product Identifiers
Publisher
MIT Press
ISBN-10
0262038390
ISBN-13
9780262038393
eBay Product ID (ePID)
243128698
Product Key Features
Number of Pages
448 Pages
Language
English
Publication Name
Mathematics of Big Data : Spreadsheets, Databases, Matrices, and Graphs
Subject
Computer Science, General, Databases / Data Mining
Publication Year
2018
Type
Textbook
Subject Area
Mathematics, Computers
Series
Mit Lincoln Laboratory Ser.
Format
Hardcover
Dimensions
Item Height
1.2 in
Item Weight
35.3 Oz
Item Length
9.4 in
Item Width
7.3 in
Additional Product Features
Intended Audience
Trade
LCCN
2017-057054
Illustrated
Yes
Synopsis
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., 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 Number
QA76.9.B45K47 2018
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