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)


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.


Collaborative data mining Intelligent home appliance Collaborative network 



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


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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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