Name: Mathematics of big Data: Spreadsheets, Databases, Matrices, and Graphs (Mit Lincoln Laboratory Series) (en Inglés)
SKU: 50566679
Price: 69.20 USD
Availability: InStock
Author: Jeremy Kepner; Hayden Jananthan
ISBN: 9780262038393

portada Mathematics of big Data: Spreadsheets, Databases, Matrices, and Graphs (Mit Lincoln Laboratory Series) (en Inglés)

Mathematics of big Data: Spreadsheets, Databases, Matrices, and Graphs (Mit Lincoln Laboratory Series) (en Inglés)

Formato

Libro Físico

Editorial

The Mit Press

Autor

Jeremy Kepner; Hayden Jananthan

Año

2018

Idioma

Inglés

N° páginas

448

Encuadernación

Tapa Dura

ISBN13

9780262038393

Categorías

Ciencias de la computación

Mathematics of big Data: Spreadsheets, Databases, Matrices, and Graphs (Mit Lincoln Laboratory Series) (en Inglés)

Jeremy Kepner; Hayden Jananthan (Autor) · The Mit Press · Tapa Dura

Libro Nuevo Importado

Envío: 17 a 27 días háb.

$ 125.82$ 69.20

-45%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 20 unidades

$ 69.20

Llega entre el 23 Sep y el 12 Oct a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Mathematics of big Data: Spreadsheets, Databases, Matrices, and Graphs (Mit Lincoln Laboratory Series) (en Inglés)"

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.