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Collaborative Data Mining for Intelligent Home Appliances

  • Oliviu MateiEmail author
  • Giovanni Di Orio
  • Javad Jassbi
  • José Barata
  • Claudio Cenedese
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 480)

Abstract

The augmentation of physical devices and resources with electronics, software, sensing elements and network connectivity is a “hot topic” as confirmed also by the several research projects and activities on internet-of-things (IoT) and cyber-physical systems (CPS) research streams. It is obvious that intelligent products are taking more responsibility in future collaborative networks. Recent products are becoming more and more intelligent and connected by using the existing network infrastructure, meaning that products are becoming active agents in networks and valuable data sources that are capable to provide data continuously during their operation. This is leading to a massive amount of data that can be used by product manufacturers to be and remain competitive in market sharing. In this scenario, the application of collaborative data mining techniques, supported by machine learning algorithms, is aimed to enable the analysis of the data provided from multiple and above all distributed data sources in order to discover and extract useful knowledge about the behavior of the users along with the usage patterns of their devices and appliances.

Keywords

Collaborative data mining Intelligent home appliance Collaborative network 

Notes

Acknowledgments

This work is partly supported by the ProSEco project of EU’s 7th FP, under the grant agreement no. NMP-2013 609143.

References

  1. 1.
    Picard, W.: Resilient and robust human-agent collectives: a network perspective. In: Camarinha-Matos, L.M., et al. (eds.) PRO-VE 2015. IFIP AICT, vol. 463, pp. 79–87. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24141-8_7 CrossRefGoogle Scholar
  2. 2.
    Sanou, B.: “ITC Facts and Figures 2013”, Telecommunication Development Bureau, International Telecommunications Union (ITU), Geneva, February 2013. Accessed 23 May 2015Google Scholar
  3. 3.
    Matei, O., Nagorny, K., Stoebener, K.: Applying data mining in the context of Industrial Internet. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 7(1) (2016). http://dx.doi.org/10.14569/IJACSA.2016.070184
  4. 4.
    Di Orio, G., Matei, O., Scholze, S., Stokic, D., Barata, J., Cenedese, C.: A platform to support the product servitization. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 7(2) (2016). http://dx.doi.org/10.14569/IJACSA.2016.070254
  5. 5.
    Matei, O.: Preliminary results of the analysis of field data from ovens. Carpathian J. Electr. Eng. 8(1) (2014)Google Scholar
  6. 6.
    Appleman, K.H., et al.: Collaborative internet data mining systems. US Patent 5,918,010, 29 June 1999Google Scholar
  7. 7.
    Maimon, O., Rokach, L. (eds.): Data Mining and Knowledge Discovery Handbook, vol. 2. Springer, New York (2005)zbMATHGoogle Scholar
  8. 8.
    Moyle, S.: Collaborative data mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 1029–1039. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Zhan, J.: Privacy-preserving collaborative data mining. IEEE Comput. Intell. Mag. 3(2), 31–41 (2008)CrossRefGoogle Scholar
  10. 10.
    Haythornthwaite, C.: Social networks and Internet connectivity effects. Inf. Community Soc. 8(2), 125–147 (2005)CrossRefGoogle Scholar
  11. 11.
    Heierman III, E.O., Cook, D.J.: Improving home automation by discovering regularly occurring device usage patterns. In: Third IEEE International Conference on Data Mining, ICDM 2003. IEEE (2003)Google Scholar
  12. 12.
    Hajibandeh, N., et al.: Resemblance measurement of electricity market behavior based on a data distribution model. Int. J. Electr. Power Energy Syst. 78, 547–554 (2016)CrossRefGoogle Scholar
  13. 13.
    Camarinha-Matos, L.M., Afsarmanesh, H.: Collaboration forms. In: Camarinha-Matos, L.M., Afsarmanesh, H. (eds.) Collaborative Networks: Reference Modeling, pp. 51–66. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Camarinha-Matos, L.M., Afsarmanesh, H.: Motivation for a theoretical foundation for collaborative networks. In: Camarinha-Matos, L.M., Afsarmanesh, H. (eds.) Collaborative Networks: Reference Modeling, pp. 5–14. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Di Orio, G., Barata, D., Rocha, A., Barata, J.: A cloud-based infrastructure to support manufacturing resources composition. In: Camarinha-Matos, L.M., Baldissera, T.A., Di Orio, G., Marques, F. (eds.) DoCEIS 2015. IFIP AICT, vol. 450, pp. 82–89. Springer, Heidelberg (2015)Google Scholar
  16. 16.
    Wei, W.W.S. (ed.): Time Series Analysis. Addison-Wesley, Reading (1994)Google Scholar
  17. 17.
    Aarts, R.M., Irwan, R., Janssen, A.J.E.M.: Efficient tracking of the cross-correlation coefficient. IEEE Trans. Speech Audio Process. 10(6), 391–402 (2002)CrossRefGoogle Scholar
  18. 18.
    Bourke, P.: Cross Correlation, Auto Correlation—2D Pattern Identification (1996)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Oliviu Matei
    • 1
    Email author
  • Giovanni Di Orio
    • 2
  • Javad Jassbi
    • 2
  • José Barata
    • 2
  • Claudio Cenedese
    • 3
  1. 1.Department of Electrical EngineeringTechnical University of Cluj-Napoca, North University Center of Baia MareBaia MareRomania
  2. 2.UNINOVA-CTS, Department of Electrical EngineeringFCT-UNLCaparicaPortugal
  3. 3.Global Technology Center – GTC, Electrolux SpaPordenoneItaly

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