TY - GEN
T1 - 99-110 Mining appliance usage patterns in smart home environment
AU - Chen, Yi Cheng
AU - Ko, Yu Lun
AU - Peng, Wen-Chih
AU - Lee, Wang Chien
PY - 2013
Y1 - 2013
N2 - Nowadays, due to the great advent of sensor technology, the data of all appliances in a house can be collected easily. However, with a huge amount of appliance usage log data, it is not an easy task for residents to visualize how the appliances are used. Mining algorithms is necessary to discover appliance usage patterns that capture representative usage behavior of appliances. If some of our representative patterns of appliance electricity usages are available, we may be able to adapt our usage behaviors to conserve the energy easily. In this paper, we introduce (i) two types of usage patterns which capture the representative usage behaviors of appliances in a smart home environment and (ii) the corresponding algorithms for discovering usage patterns efficiently. Finally, we apply our algorithms on a real-world dataset to show the practicability of usage pattern mining.
AB - Nowadays, due to the great advent of sensor technology, the data of all appliances in a house can be collected easily. However, with a huge amount of appliance usage log data, it is not an easy task for residents to visualize how the appliances are used. Mining algorithms is necessary to discover appliance usage patterns that capture representative usage behavior of appliances. If some of our representative patterns of appliance electricity usages are available, we may be able to adapt our usage behaviors to conserve the energy easily. In this paper, we introduce (i) two types of usage patterns which capture the representative usage behaviors of appliances in a smart home environment and (ii) the corresponding algorithms for discovering usage patterns efficiently. Finally, we apply our algorithms on a real-world dataset to show the practicability of usage pattern mining.
KW - Appliance usage pattern
KW - Interval-based sequential pattern
KW - Smart environment
UR - http://www.scopus.com/inward/record.url?scp=84893560474&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37453-1_9
DO - 10.1007/978-3-642-37453-1_9
M3 - Conference contribution
AN - SCOPUS:84893560474
SN - 9783642374524
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 99
EP - 110
BT - Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
T2 - 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Y2 - 14 April 2013 through 17 April 2013
ER -