Innovations in Big Data Mining and Embedded Knowledge
暫譯: 大數據挖掘與嵌入式知識的創新

Esposito, Anna, Esposito, Antonietta M., Jain, Lakhmi C.

  • 出版商: Springer
  • 出版日期: 2019-07-16
  • 售價: $6,340
  • 貴賓價: 9.5$6,023
  • 語言: 英文
  • 頁數: 265
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3030159388
  • ISBN-13: 9783030159382
  • 相關分類: 嵌入式系統大數據 Big-dataData-mining
  • 海外代購書籍(需單獨結帳)

相關主題

商品描述

This book addresses the usefulness of knowledge discovery through data mining. With this aim, contributors from different fields propose concrete problems and applications showing how data mining and discovering embedded knowledge from raw data can be beneficial to social organizations, domestic spheres, and ICT markets.

Data mining or knowledge discovery in databases (KDD) has received increasing interest due to its focus on transforming large amounts of data into novel, valid, useful, and structured knowledge by detecting concealed patterns and relationships.

The concept of knowledge is broad and speculative and has promoted epistemological debates in western philosophies. The intensified interest in knowledge management and data mining stems from the difficulty in identifying computational models able to approximate human behaviors and abilities in resolving organizational, social, and physical problems. Current ICT interfaces are not yet adequately advanced to support and simulate the abilities of physicians, teachers, assistants or housekeepers in domestic spheres. And unlike in industrial contexts where abilities are routinely applied, the domestic world is continuously changing and unpredictable. There are challenging questions in this field: Can knowledge locked in conventions, rules of conduct, common sense, ethics, emotions, laws, cultures, and experiences be mined from data? Is it acceptable for automatic systems displaying emotional behaviors to govern complex interactions based solely on the mining of large volumes of data?

Discussing multidisciplinary themes, the book proposes computational models able to approximate, to a certain degree, human behaviors and abilities in resolving organizational, social, and physical problems.

The innovations presented are of primary importance for:

a. The academic research community

b. The ICT market

c. Ph.D. students and early stage researchers

d. Schools, hospitals, rehabilitation and assisted-living centers

e. Representatives from multimedia industries and standardization bodies

商品描述(中文翻譯)

這本書探討了透過資料挖掘進行知識發現的實用性。為此,來自不同領域的貢獻者提出具體的問題和應用,展示資料挖掘及從原始資料中發現隱含知識如何對社會組織、家庭領域和資訊通信技術(ICT)市場帶來益處。

資料挖掘或資料庫中的知識發現(KDD)因其專注於將大量資料轉化為新穎、有效、有用且結構化的知識,透過檢測隱藏的模式和關係而受到越來越多的關注。

知識的概念廣泛且具推測性,並在西方哲學中促進了認識論的辯論。對知識管理和資料挖掘的興趣加劇,源於識別能夠近似人類行為和能力的計算模型在解決組織、社會和物理問題方面的困難。目前的ICT介面尚未足夠先進,無法支持和模擬醫生、教師、助理或家庭工作者在家庭領域的能力。與工業環境中能力的常規應用不同,家庭世界不斷變化且難以預測。在這個領域中存在著挑戰性問題:是否可以從資料中挖掘被鎖定在慣例、行為準則、常識、倫理、情感、法律、文化和經驗中的知識?自動系統是否可以僅基於大量資料的挖掘來管理複雜的互動,並顯示情感行為,這是否可接受?

本書討論多學科主題,提出能夠在一定程度上近似人類行為和能力的計算模型,以解決組織、社會和物理問題。

所提出的創新對以下群體具有重要意義:

a. 學術研究社群

b. ICT市場

c. 博士生和早期研究人員

d. 學校、醫院、復健和輔助生活中心

e. 多媒體產業和標準化機構的代表