Authors: Yi-Cheng Chen; Shih-Hao Chang; Wei-Hsun Liao; Jianquan Liu; Yutaka Watanobe
Addresses: Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan ' Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan ' Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan ' Central Research Laboratories, NEC Corporation, Tokyo, Japan ' Department of Computer Science and Engineering, University of Aizu, Fukushima, Japan
Abstract: Nowadays, owing to the great advent of sensor technology, data can be collected easily. Mining Internet of Things (IoT) data has attracted researchers' attention owing to its practicability. Mining smart home data is one significant application in the IoT domain. Generally, the usage data of appliances in a smart environment are generated progressively; visualising how appliances are used from huge amount of data is a challenging issue. Hence, an algorithm is needed to dynamically discover appliance usage patterns. Prior studies on usage pattern discovery are mainly focused on discovering patterns while ignoring the dynamic maintenance of mined results. In this paper, a cloud-based system, Dynamic Correlation Mining System (DCMS), is developed to incrementally capture the usage correlations among appliances in a smart home environment. Furthermore, several pruning strategies are proposed to effectively reduce the search space. Experimental results indicate that the developed system is efficient in execution time and possesses great scalability. Subsequent application of DCMS on a real data set also demonstrates the practicability of mining smart home data.
Keywords: incremental mining; usage relations; sequential patterns; smart homes; cloud computing; appliance usage patterns; data mining; internet of things; IoT; smart home data; home appliances.
International Journal of Web and Grid Services, 2016 Vol.12 No.3, pp.257 - 272
Received: 04 Dec 2015
Accepted: 02 Jun 2016
Published online: 14 Sep 2016 *