A cloud-based system for dynamically capturing appliance usage relations

doi:10.1504/IJWGS.2016.079161

機構典藏 > College of Engineering > Graduate Institute & Department of Computer Science and Information Engineering > Journal Article > Item 987654321/108678

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/108678

Title:	A cloud-based system for dynamically capturing appliance usage relations
Authors:	Yi-Cheng Chen;Shih-Hao Chang;Wei-Hsun Liao;Jianquan Liu;Yutaka Watanobe
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
Date:	2016-09-22
Issue Date:	2016-12-03 02:10:15 (UTC+8)
Publisher:	Inderscience Publishers
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.
Relation:	International Journal of Web and Gird Services 12(3), p.257-272
DOI:	10.1504/IJWGS.2016.079161
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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