Innovations in Big Data Mining and Embedded Knowledge

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

Part of the Intelligent Systems Reference Library book series (ISRL, volume 159)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Anna Esposito, Antonietta M. Esposito, Lakhmi C. Jain
    Pages 1-11
  3. Valerio Bellandi, Paolo Ceravolo, Ernesto Damiani, Eugenio Tacchini
    Pages 13-33
  4. Uday Kamath, Carlotta Domeniconi, Amarda Shehu, Kenneth De Jong
    Pages 35-59
  5. Alessandro Vinciarelli, Walter Riviera, Francesca Dalmasso, Stefan Raue, Chamila Abeyratna
    Pages 83-97
  6. Maria Koutsombogera, Carl Vogel
    Pages 99-115
  7. Giorgio Leonardi, Stefania Montani, Luigi Portinale, Silvana Quaglini, Manuel Striani
    Pages 117-136
  8. Erwan Moreau, Carl Vogel, Marguerite Barry
    Pages 137-166
  9. Costanza Navarretta, Lucretia Oemig
    Pages 167-184
  10. Giuseppe Placidi, Luigi Cinque, Matteo Polsinelli
    Pages 185-202
  11. Ronald Böck, Olga Egorow, Juliane Höbel-Müller, Alicia Flores Requardt, Ingo Siegert, Andreas Wendemuth
    Pages 203-233
  12. Shahin Amiriparian, Maximilian Schmitt, Simone Hantke, Vedhas Pandit, Björn Schuller
    Pages 235-257
  13. Back Matter
    Pages 275-276

About this book


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


Knowledge Discovery Domestic Spheres Social Robots Big Data Models Complex Autonomous Systems Socially Believable Interfaces

Editors and affiliations

  • Anna Esposito
    • 1
  • Antonietta M. Esposito
    • 2
  • Lakhmi C. Jain
    • 3
  1. 1.Dipartimento di Psicologia and International Institute for Advanced Scientific Studies (IIASS)Università degli Studi della Campania “Luigi Vanvitelli”CasertaItaly
  2. 2.Sezione di Napoli, Osservatorio VesuvianoIstituto Nazionale di Geofisica e VulcanologiaNapoliItaly
  3. 3.University of CanberraCanberraAustralia

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