Название: Innovations in Big Data Mining and Embedded Knowledge
Автор: Anna Esposito, Antonietta M. Esposito, Lakhmi C. Jain
Издательство: Springer
Год: 2019
Страниц: 286
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

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.

Data Mining has received a great momentum of interest due to the automatic processes transforming big amount of data into novel, valid, useful, and structured knowledge by detecting concealed patterns and relationships in data. However, embedded knowledge has not been thoroughly considered in data mining.

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?