Collaborative Data Mining for Intelligent Home Appliances

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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download

https://hal.inria.fr/hal-01614574
Contributor : Hal Ifip <>
Submitted on : Wednesday, October 11, 2017 - 10:39:52 AM
Last modification on : Thursday, October 10, 2019 - 2:54:02 PM
Long-term archiving on : Friday, January 12, 2018 - 1:30:53 PM

File

430868_1_En_27_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Oliviu Matei, Giovanni Orio, Javad Jassbi, José Barata, Claudio Cenedese. Collaborative Data Mining for Intelligent Home Appliances. 17th Working Conference on Virtual Enterprises (PRO-VE), Oct 2016, Porto, Portugal. pp.313-323, ⟨10.1007/978-3-319-45390-3_27⟩. ⟨hal-01614574⟩

Share

Metrics

Record views

138

Files downloads

237