Recently, due to the great advent of sensor technology, residents can collect household appliance usage data easily. However, in general, usage data are generated progressively; visualizing how appliances are used from huge amount of data is challenging. Thus, an algorithm is needed to incrementally discover appliance usage patterns. Prior studies on usage pattern discovery are mainly focused on mining patterns while ignoring the incremental maintenance of mined results. In this paper, a novel method, Dynamic Correlation Miner (DCMiner), is developed to incrementally capture and maintain the usage correlations among appliances in a smart home environment. Furthermore, several optimization techniques are proposed to effectively reduce the search space. Experimental results indicate that the proposed method is efficient in execution time and possesses great scalability. Subsequent application of DCMiner on a real dataset also demonstrates its practicability.
Relation:
The 18th International Conference on Network-Based Information Systems (NBiS 2015)
