Mathematics of Big Data: Spreadsheets, Databases, Matrices, and Graphs (Hardcover)
Jeremy Kepner, Hayden Jananthan
- 出版商: MIT
- 出版日期: 2018-07-17
- 售價: $1,420
- 貴賓價: 9.5 折 $1,349
- 語言: 英文
- 頁數: 448
- 裝訂: Hardcover
- ISBN: 0262038390
- ISBN-13: 9780262038393
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相關分類:
大數據 Big-data、資料庫
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相關主題
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)
這是一本首次介紹大數據分析的共同數學基礎的書籍,涵蓋了各種應用和技術。
如今,在包括互聯網搜索、醫療保健、金融、社交媒體、無線設備和網絡安全等領域,數據的量、速度和多樣性正在迅速增加。事實上,這些數據的增長速度已超出我們分析的能力。為應對這一挑戰,我們開發了一系列工具,包括電子表格、數據庫、矩陣和圖形,這些工具都反映了將數據作為整體集合而不是個別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。關聯數組統一並簡化數據,使讀者能夠超越各種工具之間的差異,利用它們在數學上的相似性來解決最困難的大數據挑戰。
本書首先以實際的方式介紹了關聯數組的概念,介紹了關聯數組操作系統D4M(動態分佈式維度數據模型),並描述了關聯數組在圖形分析和機器學習中的應用。它提供了關聯數組的數學嚴格定義,並描述了從這一定義中產生的關聯數組的特性。最後,本書展示了如何將線性概念擴展到包括關聯數組在內。《大數據的數學》可供工程師、科學家、數學家、計算機科學家和軟件工程師作為教材或參考書使用,用於分析大數據。