Abstract
LBS(Location-Based Service) is generally described as an information service that provides location-based information to its mobile users. Since the conventional studies on data mining do not consider spatial and temporal aspects of data simultaneously, these techniques have limited application in studying the moving objects of LBS with respect to the spatial attributes that is changing over time. In this paper, we propose a new data mining technique and algorithms for identifying temporal patterns from series of locations of moving objects that have temporal and spatial dimensions. For this purpose, we use the spatial operation to generalize a location of moving point, applying time constraints between locations of moving objects to make valid moving sequences. Finally, we show that our technique generates temporal patterns found in frequent moving sequences.
This work was supported in part by KOSEF RRC(Cheongju Univ. ICRC) and KISTI Bioinformatics Research Center.
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Chung, J.D., Paek, O.H., Lee, J.W., Ryu, K.H. (2002). Temporal Pattern Mining of Moving Objects for Location-Based Service. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds) Database and Expert Systems Applications. DEXA 2002. Lecture Notes in Computer Science, vol 2453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46146-9_33
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DOI: https://doi.org/10.1007/3-540-46146-9_33
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