Skip to main content
SpringerLink
Log in
Book cover

Innovations in Big Data Mining and Embedded Knowledge pp 1–11Cite as

More Than Data Mining

  • Chapter
  • First Online:

  • 499 Accesses

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

Abstract

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 chapters reported in this book discuss on several facets of embedded knowledge and propose solutions for data mining.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Amiriparian, S., Pugachevskiy, S., Cummins, N., Hantke, S., Pohjalainen, J., Keren, G., Schuller, B.: CAST a database: rapid targeted large-scale big data acquisition via small-world modelling of social media platforms. In: Proceedings Biannual Conference on Affective Computing and Intelligent Interaction(ACII), San Antonio, TX, pp. 340–345 (2017)

    Google Scholar 

  2. Amiriparian, S., Schmitt, M., Hantke, S., Pandit, V., Schuller, B.: Humans inside: cooperative big multimedia data mining. This volume (2019)

    Google Scholar 

  3. Baron-Cohen, S., Wheelwright, S.: The empathy quotient: an investigation of adults with asperger syndrome or high functioning autism, and normal sex differences. J. Autism Dev. Disord. 34(2), 163–175 (2004)

    Article  Google Scholar 

  4. Baron-Cohen, S., Richler, J., Bisarya, D., Gurunathan, N., Wheelwright, S.: The systemizing quotient: an investigation of adults with asperger syndrome or high functioning autism, and normal sex differences. Philos. Trans. R. Soc. Lond. B Biol. Sci. 358(1430), 361–374 (2003)

    Article  Google Scholar 

  5. Barsalou, L.W., Niedenthal, P.M., Barbey, A.K., Ruppert, J.A.: Social embodiment. In: Ross, B.H. (ed.) The Psychology of Learning and Motivation, 43, 43–92. San Diego, Academic Press (2003)

    Google Scholar 

  6. Bellandi, V., Ceravolo, P., Damiani, E., Tacchini, E.: Designing a recommender system for touristic activities in a big data as a service platform. This volume (2019)

    Google Scholar 

  7. Berrada, G., van Keulen, M., Habib, M.: Hadoop for EEG storage and processing: a feasibility study. In: Brain Informatics and Health, 218–230 (2014)

    Google Scholar 

  8. Böck, R., Egorow, O., Höbel-Müller, J., Flores-Requardt, A., Siegert, I., Wendemuth, A.: Anticipating the user: acoustic disposition recognition in intelligent interactions. This volume (2019)

    Google Scholar 

  9. Botha, A., Kourie, D., Snyman, R.: Coping with Continuous Change in the Business Environment, Knowledge Management and Knowledge Management Technology. Chandice Publishing Ltd., London (2008)

    Google Scholar 

  10. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  11. Davis, R., Mauer, L.J.: Fourier transform infrared (FTIR)-spectroscopy: a rapid tool for detection and analysis of foodborne pathogenic bacteria. Curr. Res. Technol. Educ. Top. Appl. Microbiol. Microb. Biotechnol. 2, 1582–1594 (2010)

    Google Scholar 

  12. Dean, J., Ghemawat, S.: MapReduce: a flexible data processing tool. Commun. ACM 53(1), 72 (2010)

    Article  Google Scholar 

  13. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B. 39(1), 1–38 (1977). JSTOR 2984875. MR 0501537

    MathSciNet  MATH  Google Scholar 

  14. Esposito, A.: COST 2102: cross-modal analysis of verbal and nonverbal communication (CAVeNC). In: Esposito, A. et al. (eds.) Verbal and nonverbal communication behaviours, LNCS, vol. 4775, 1–10, Springer, Basel, Switzerland (2007)

    Google Scholar 

  15. Esposito, A., Fortunati, L., Lugano, G.: Modeling emotion, behaviour and context in socially believable robots and ICT interfaces. Cogn. Comput. 6(4), 623–627 (2014)

    Article  Google Scholar 

  16. Esposito, A., Esposito, A.M.: On the recognition of emotional vocal expressions: motivations for an holistic approach. Cogn. Process. 13(2), 541–550 (2012)

    Article  Google Scholar 

  17. Fortunati, L., Esposito, A., Lugano, G.: Beyond Industrial robotics: social robots entering public and domestic spheres. Inf. Soc. 31(3), 229–23 (2015)

    Google Scholar 

  18. Gamble, P.R., Blackwell, J.: Knowledge Management: A State of the Art Guide. London, Kogan Page (2001)

    Google Scholar 

  19. Ganimian, A.J., Koretz, D.M.: Dataset of International Large-Scale Assessments. Cambridge, Harvard Graduate School of Education (2017). Last updated: 8 Feb 2017

    Google Scholar 

  20. Gnjatović, M.: Conversational agents and negative lessons from behaviourism. This volume (2019)

    Google Scholar 

  21. Gustafsson, J.-E.: Effects of international comparative studies on educational quality on the quality of educational research. Eur. Educ. Res. J. 7(1), 1–17 (2008). www.wwwords.eu/EERJ

  22. Hantke, S., Appel, T., Schuller, B.: The inclusion of gamification solutions to enhance user enjoyment on crowdsourcing platforms. In: Proceedings of the 1st Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia 2018), IEEE, Beijing, People’s Republic of China (2018)

    Google Scholar 

  23. Hofstede, G.: Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations. 2nd edn, Thousand Oaks, Sage (2001)

    Google Scholar 

  24. Hofstede, G.: Dimensionalizing cultures: the Hofstede model in context. Online Readings in Psychol. Cult. 2(1) (2011). https://doi.org/10.9707/2307-0919.1014

  25. Kamath, U., Domeniconi, C., Shehu, A., De Jong, K.: EML: a scalable, transparent meta-learning paradigm for big data applications. This volume (2019)

    Google Scholar 

  26. Kapros, E.: Towards addressing the limitations of educational policy based on international large-scale assessment data with Castoriadean magmas. This volume (2019)

    Google Scholar 

  27. Kemsley, E.K., Holland, J.K., Defernez, M., Wilson, R.H.: Detection of adulteration of raspberry purees using infrared spectroscopy and chemometrics. J. Agric. Food Chem. 44, 3864–3870 (1996)

    Article  Google Scholar 

  28. Koutsombogera, M., Vogel, C.: Speech pause patterns in collaborative dialogs. This volume (2019)

    Google Scholar 

  29. Leonardi, G., Montani, S., Portinale, L., Quaglini, S., Striani, M.: Discovering knowledge embedded in bio-medical databases: experiences in food characterization and in medical process mining. This volume (2019)

    Google Scholar 

  30. Lloyd, S.P.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)

    Article  MathSciNet  Google Scholar 

  31. Madden, S.: From databases to big data. IEEE Internet Comput. 16, 4–6 (2012)

    Article  Google Scholar 

  32. Moreau, E., Vogel, C., Barry, M.: A paradigm for democratizing artificial intelligence research. This volume (2019)

    Google Scholar 

  33. Navarretta, C., Oemig, L.: Big data and multimodal communication: a perspective view. This volume (2019)

    Google Scholar 

  34. Nonaka, I.: Theory of organizational knowledge creation. Organ. Sci. 5(1), 14–37 (1994)

    Article  Google Scholar 

  35. Placidi, G., Cinque, L., Polsinelli, M.: A web application for characterizing spontaneous emotions using long EEG recording sessions. This volume (2019)

    Google Scholar 

  36. Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C.J.C., Smola, A. (eds.) Advances in Kernel Methods, 185–208. Cambridge, MIT Press (1999)

    Google Scholar 

  37. Smith, E.R., Semin, G.R.: Socially situated cognition: cognition in its social context. Adv. Exp. Soc. Psychol. 36, 53–117 (2004)

    Google Scholar 

  38. Squartini, S., Esposito, A.: CO-worker: toward real-time and context-aware systems for human collaborative knowledge building. Cogn. Comput. 4(2), 157–171 (2012). https://doi.org/10.1007/s12559-012-9136-5

    Article  Google Scholar 

  39. Vinciarelli, A., Riviera, W., Dalmasso, F., Raue, S., Abeyratna, C.: What do prospective students want? An observational study of preferences about subject of study in higher education. This volume (2019)

    Google Scholar 

  40. Vinciarelli, A., Esposito, A., André, E., Bonin, F., Chetouani, M., Cohn, J.F., Cristan, M., Fuhrmann, F., Gilmartin, E., Hammal, Z., Heylen, D., Kaiser, R., Koutsombogera, M., Potamianos, A., Renals, S., Riccardi, G., Salah, A.A.: Open challenges in modelling, analysis and synthesis of human behaviour in human-human and human-machine interactions. Cogn. Comput. 7(4), 397–413 (2015)

    Article  Google Scholar 

  41. Vogel, C., Esposito, A.: Advancing and validating models of cognitive architecture, unpublished manuscript (2017)

    Google Scholar 

  42. Wagemaker, H.: International large-scale assessments: from research to policy. In: Rutkowski, L. et al. (eds.) Handbook of International Large-Scale Assessment. Background, Technical Issues, and Methods of Data Analysis, 11–36. Boca Raton, CRC Press (2014). https://ilsa-gateway.org/ilsa-in-education

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Psicologia and International Institute for Advanced Sceintific Studies (IIASS), Università della Campania “Luigi Vanvitelli”, Caserta, Italy

    Anna Esposito

  2. Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Napoli Osservatorio Vesuviano, Naples, Italy

    Antonietta M. Esposito

  3. University of Technology Sydney, Sydney, Australia

    Lakhmi C. Jain

  4. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

  5. Liverpool Hope University, Liverpool, UK

    Lakhmi C. Jain

Authors
  1. Anna Esposito
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonietta M. Esposito
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lakhmi C. Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anna Esposito .

Editor information

Editors and Affiliations

  1. Dipartimento di Psicologia and International Institute for Advanced Scientific Studies (IIASS), Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy

    Anna Esposito

  2. Sezione di Napoli, Osservatorio Vesuviano, Istituto Nazionale di Geofisica e Vulcanologia, Napoli, Italy

    Antonietta M. Esposito

  3. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Esposito, A., Esposito, A.M., Jain, L.C. (2019). More Than Data Mining. In: Esposito, A., Esposito, A., Jain, L. (eds) Innovations in Big Data Mining and Embedded Knowledge. Intelligent Systems Reference Library, vol 159. Springer, Cham. https://doi.org/10.1007/978-3-030-15939-9_1

Download citation

Publish with us

Policies and ethics

Search

Navigation