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Innovations in big data mining and e...

Esposito, Anna.

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Innovations in big data mining and embedded knowledge

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Innovations in big data mining and embedded knowledge/ edited by Anna Esposito, Antonietta M. Esposito, Lakhmi C. Jain.
其他作者:	Esposito, Anna.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xix, 276 p. :ill., digital ;24 cm.
內容註:	Designing a Recommender System for Touristic Activities in a Big Data as a Service Platform -- A Scalable, Transparent Meta-Learning Paradigm for Big Data Applications -- Towards Addressing the Limitations of Educational Policy based on International Large-scale Assessment Data with Castoriadean Magmas -- What do Prospective Students Want? An Observational Study of Preferences About Subject of Study in Higher Education -- Speech Pause Patterns in Collaborative Dialogs.
Contained By:	Springer eBooks
標題:	Big data. -
電子資源:	https://doi.org/10.1007/978-3-030-15939-9
ISBN:	9783030159399

Innovations in big data mining and embedded knowledge
Innovations in big data mining and embedded knowledge[electronic resource] /edited by Anna Esposito, Antonietta M. Esposito, Lakhmi C. Jain. - Cham :Springer International Publishing :2019. - xix, 276 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1591868-4394 ;. - Intelligent systems reference library ;v.159..

Designing a Recommender System for Touristic Activities in a Big Data as a Service Platform -- A Scalable, Transparent Meta-Learning Paradigm for Big Data Applications -- Towards Addressing the Limitations of Educational Policy based on International Large-scale Assessment Data with Castoriadean Magmas -- What do Prospective Students Want? An Observational Study of Preferences About Subject of Study in Higher Education -- Speech Pause Patterns in Collaborative Dialogs.

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.

ISBN: 9783030159399

Standard No.: 10.1007/978-3-030-15939-9doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45 / I566 2019

Dewey Class. No.: 005.7

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520 $a 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.
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