Mathematics of Big Data: Spreadsheets, Databases, Matrices, and Graphs (Hardcover)
暫譯: 大數據的數學:電子表格、資料庫、矩陣與圖形 (精裝版)

Jeremy Kepner, Hayden Jananthan

  • 出版商: MIT
  • 出版日期: 2018-07-17
  • 定價: $1,420
  • 售價: 9.8$1,392
  • 語言: 英文
  • 頁數: 448
  • 裝訂: Hardcover
  • ISBN: 0262038390
  • ISBN-13: 9780262038393
  • 相關分類: 大數據 Big-data資料庫
  • 立即出貨 (庫存=1)

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商品描述

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.

商品描述(中文翻譯)

第一本介紹大數據分析在各種應用和技術中共同數學基礎的書籍。

如今,數據的體量、速度和多樣性在各個領域迅速增加,包括網際網路搜尋、醫療保健、金融、社交媒體、無線設備和網路安全。事實上,這些數據的增長速度超出了我們分析它們的能力。為了解決這一挑戰而開發的工具——包括電子表格、數據庫、矩陣和圖形——都反映了需要將數據作為整體集合而非單個元素來存儲和操作的需求。本書介紹了這些數據集的共同數學基礎,這些基礎適用於許多應用和技術。關聯陣列統一並簡化了數據,使讀者能夠超越各種工具之間的差異,利用它們的數學相似性來解決最棘手的大數據挑戰。

本書首先以實際的方式介紹關聯陣列的概念,介紹關聯陣列操作系統 D4M(動態分佈式維度數據模型),並描述關聯陣列在圖形分析和機器學習中的應用。它提供了關聯陣列的數學嚴謹定義,並描述了由此定義產生的關聯陣列的特性。最後,本書展示了如何將線性概念擴展到涵蓋關聯陣列。《大數據的數學》可作為教科書或參考書,供分析大數據的工程師、科學家、數學家、計算機科學家和軟體工程師使用。