Collaborative Data Mining for Intelligent Home Appliances
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.
KeywordsCollaborative 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.
- 2.Sanou, B.: “ITC Facts and Figures 2013”, Telecommunication Development Bureau, International Telecommunications Union (ITU), Geneva, February 2013. Accessed 23 May 2015Google Scholar
- 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.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.Matei, O.: Preliminary results of the analysis of field data from ovens. Carpathian J. Electr. Eng. 8(1) (2014)Google Scholar
- 6.Appleman, K.H., et al.: Collaborative internet data mining systems. US Patent 5,918,010, 29 June 1999Google Scholar
- 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
- 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.Wei, W.W.S. (ed.): Time Series Analysis. Addison-Wesley, Reading (1994)Google Scholar
- 18.Bourke, P.: Cross Correlation, Auto Correlation—2D Pattern Identification (1996)Google Scholar