ÇѱÛÁ¦¸ñ(Korean Title) |
À̵¿°´Ã¼ À§Ä¡ ÀϹÝȸ¦ ÀÌ¿ëÇÑ ½Ã°ø°£ À̵¿ÆÐÅÏ Å½»ç |
¿µ¹®Á¦¸ñ(English Title) |
Spatiotemporal Moving Pattern Discovery using Location Generalization of Moving Objects |
ÀúÀÚ(Author) |
ÀÌÁØ¿í
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¿ø¹®¼ö·Ïó(Citation) |
VOL 10-D NO. 07 PP. 1103 ~ 1114 (2003. 12) |
Çѱ۳»¿ë (Korean Abstract) |
ÇöÀçÀÇ À̵¿°´Ã¼¸¦ ±â¹ÝÀ¸·Î ÇÏ´Â ´Ù¾çÇÑ ½Ã°ø°£ ÀÀ¿ëȯ°æ¿¡¼ÀÇ ¼ºñ½º Áö¿ø ½Ã½ºÅÛ °³¹ßÀ» À§ÇÏ¿© Áß¿äÇÑ ¹®Á¦ ÁßÀÇ Çϳª´Â ¹æ´ëÇÑ À̵¿°´Ã¼ÀÇ À§Ä¡ À̵¿ µ¥ÀÌÅͷκÎÅÍÀÇ ÀÇ¹Ì ÀÖ´Â Áö½ÄÀÎ ½Ã°ø°£ À̵¿ ÆÐÅÏÀ» Ž»çÇÏ´Â °ÍÀÌ´Ù. À̸¦ À§ÇÏ¿© ½Ã°£Àû À§»ó°ü°è, °ø°£Àû À§»ó°ü°è ±×¸®°í ½Ã°ø°£Àû À§»ó°ü°è¿¡ ´ëÇÑ Á¢±ÙÀÌ Áö½Ä Ž»ç¸¦ À§ÇÏ¿© °í·ÁµÇ¾î¾ß ÇÑ´Ù. ÀÌ ³í¹®¿¡¼´Â È¿À²ÀûÀÎ ½Ã°ø°£ À̵¿ ÆÐÅÏ Å½»ç ±â¹ýÀÎ MPMine ¾Ë°í¸®ÁòÀ» Á¦¾ÈÇÏ¿´´Ù. Á¦¾ÈÇÑ ±â¹ýÀº ½Ã°£ Á¦¾àÁ¶°Ç°ú °ø°£ Á¦¾àÁ¶°Ç µîÀ» ÇÔ²² °í·ÁÇÏ¸ç ¶ÇÇÑ °ø°£ À§»ó ¿¬»êÀÎ contain()À» ÀÌ¿ëÇÑ °ø°£ °³³äȸ¦ ¼öÇàÇÒ ¼ö ÀÖ´Ù. Á¦¾ÈÇÑ ±â¹ýÀº ±âÁ¸ÀÇ ÀϹÝÀûÀÎ ½Ã°£ ÆÐÅÏ Å½»ç ±â¹ý°ú ´Þ¸® À̵¿°´Ã¼ µ¥ÀÌÅÍ ÁýÇÕÀ¸·ÎºÎÅÍ À§Ä¡ ¿ä¾à ¹× ÀϹÝȸ¦ ÅëÇÏ¿© Ž»ö °ø°£À» ÁÙÀÏ ¼ö ÀÖ¾î È¿À²ÀûÀ¸·Î À¯¿ëÇÑ À̵¿ ÆÐÅÏÀ» Ž»çÇÒ ¼ö ÀÖ´Ù. |
¿µ¹®³»¿ë (English Abstract) |
Currently, one of the most critical issues in developing the service support system for various spatio-temporal applications is the discoverying of meaningful knowledge from the large volume of moving object data. This sort of knowledge refers to the spatiotemporal moving pattern. To discovery such knowledge, various relationships between moving objects such as temporal, spatial and spatiotemporal topological relationships needs to be considered in knowledge discovery. In this paper, we proposed an efficient method, MPMine, for discoverying spatiotemporal moving patterns. The method not only has considered both temporal constraint and spatial constrain but also performs the spatial generalization using a spatial topological operation, contain(). Different from the previous temporal pattern methods, the proposed method is able to save the search space by using the location summarization and generalization of the moving object data. Therefore, Efficient discoverying of the useful moving patterns is possible. |
Å°¿öµå(Keyword) |
½Ã°ø°£ À̵¿ÆÐÅÏ
Spatiotemporal Moving Pattern
ÆÐÅÏ Å½»ç
Pattern Discovery
½Ã°ø°£ Áö½ÄŽ»ç
Spatiotemporal Knowledge Discovery
À§Ä¡ ÀϹÝÈ
Location Generalization
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