Veracity of Big Data: Machine Learning and Other Approaches to Verifying Truthfulness
暫譯: 大數據的真實性：機器學習及其他驗證真實性的方法

Vishnu Pendyala

Veracity of Big Data: Machine Learning and Other Approaches to Verifying Truthfulness
  • 出版商: Apress
  • 出版日期: 2018-06-10
  • 售價: $1,370
  • 貴賓價: 9.5 折 $1,302
  • 語言: 英文
  • 頁數: 196
  • 裝訂: Paperback
  • ISBN: 1484236327
  • ISBN-13: 9781484236321
  • 相關分類: 大數據 Big-data、Machine Learning

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

Examine the problem of maintaining the quality of big data and discover novel solutions. You will learn the four V’s of big data, including veracity, and study the problem from various angles. The solutions discussed are drawn from diverse areas of engineering and math, including machine learning, statistics, formal methods, and the Blockchain technology. 

Veracity of Big Data serves as an introduction to machine learning algorithms and diverse techniques such as the Kalman filter, SPRT, CUSUM, fuzzy logic, and Blockchain, showing how they can be used to solve problems in the veracity domain. Using examples, the math behind the techniques is explained in easy-to-understand language.

Determining the truth of big data in real-world applications involves using various tools to analyze the available information. This book delves into some of the techniques that can be used. Microblogging websites such as Twitter have played a major role in public life, including during presidential elections. The book uses examples of microblogs posted on a particular topic to demonstrate how veracity can be examined and established. Some of the techniques are described in the context of detecting veiled attacks on microblogging websites to influence public opinion.

What You'll Learn
  • Understand the problem concerning data veracity and its ramifications
  • Develop the mathematical foundation needed to help minimize the impact of the problem using easy-to-understand language and examples
  • Use diverse tools and techniques such as machine learning algorithms, Blockchain, and the Kalman filter to address veracity issues
Who This Book Is For

Software developers and practitioners, practicing engineers, curious managers, graduate students, and research scholars

商品描述(中文翻譯)

檢視維護大數據品質的問題並發現新穎的解決方案。您將學習大數據的四個 V，包括真實性，並從不同角度研究這個問題。所討論的解決方案來自於工程和數學的多個領域，包括機器學習、統計學、形式方法和區塊鏈技術。

《大數據的真實性》作為機器學習演算法和多種技術的介紹，例如卡爾曼濾波器（Kalman filter）、SPR（Sequential Probability Ratio Test）、CUSUM、模糊邏輯和區塊鏈，展示了它們如何用於解決真實性領域的問題。通過示例，這些技術背後的數學以易於理解的語言進行解釋。

在現實應用中確定大數據的真實性涉及使用各種工具來分析可用的信息。本書深入探討了一些可以使用的技術。像 Twitter 這樣的微博網站在公共生活中扮演了重要角色，包括在總統選舉期間。本書使用針對特定主題的微博示例來演示如何檢查和確立真實性。一些技術在檢測對微博網站的隱蔽攻擊以影響公眾意見的背景下進行描述。

您將學到的內容：
- 理解與數據真實性相關的問題及其影響
- 發展所需的數學基礎，以幫助最小化該問題的影響，並使用易於理解的語言和示例
- 使用多種工具和技術，如機器學習演算法、區塊鏈和卡爾曼濾波器來解決真實性問題

本書適合對象：
軟體開發人員和從業者、在職工程師、好奇的管理者、研究生和研究學者

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