Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
ÇѱÛÁ¦¸ñ(Korean Title) |
°ø°£ Å°¿öµå À¯»çµµ ±â¹ÝÀÇ ºÎºÐÀû Áý´Ü °ø°£ Å°¿öµå ÁúÀÇó¸® ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
Partially Collective Spatial Keyword Query Processing Based on Spatial Keyword Similarity |
ÀúÀÚ(Author) |
À̾ÆÇö
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Ah Hyun Lee
Sehwa Park
Seog Park
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 48 NO. 10 PP. 1142 ~ 1153 (2021. 10) |
Çѱ۳»¿ë (Korean Abstract) |
Áý´ÜÀû °ø°£ Å°¿öµå ÁúÀÇ(collective spatial keyword query)´Â ÁúÀÇ À§Ä¡¿Í °¡±î¿ì¸é¼ Á¦½ÃµÈ Å°¿öµå ÁýÇÕÀ» ¸ðµÎ Æ÷ÇÔÇÏ´Â °ü½ÉÁöÁ¡(point of interest; POI)µéÀ» ¹ÝȯÇÑ´Ù. ÇÏÁö¸¸ °íÁ¤µÈ ¼öÀÇ ÁúÀÇ Å°¿öµå¸¦ °í·ÁÇϹǷΠ»ç¿ëÀÚÀÇ ºÎºÐ Å°¿öµå ÁýÇÕ¿¡ ´ëÇÑ ¼±È£µµ¸¦ ÃæºÐÈ÷ ¹Ý¿µÇÒ ¼ö ¾ø´Ù. µû¶ó¼ POI¸¶´Ù ¼±È£µµ¿¡ ¸Â´Â Å°¿öµå¸¦ À¯µ¿ÀûÀ¸·Î °í·ÁÇÏ´Â »õ·Î¿î ÁúÀÇÀÎ ºÎºÐÀû Áý´Ü °ø°£ Å°¿öµå ÁúÀÇ(partial collective spatial keyword query)¸¦ Á¦¾ÈÇÑ´Ù. ÀÌ ÁúÀÇ´Â Á¶ÇÕ ÃÖÀûÈ ¹®Á¦À̹ǷΠPOIÀÇ ¼ö°¡ ´Ã¾î³²¿¡ µû¶ó ¼öÇà ½Ã°£ÀÌ ±Þ°ÝÇÏ°Ô Áõ°¡ÇÑ´Ù. µû¶ó¼ ÀÌ·¯ÇÑ ¹®Á¦¸¦ ÇØ°áÇϱâ À§ÇØ ÀüüÀûÀΠŽ»ö °ø°£À» ÁÙÀÌ´Â Å°¿öµå ±â¹Ý Ž»ö ±â¹ýÀ» Á¦¾ÈÇÑ´Ù. ¶ÇÇÑ Å°¿öµåÀÇ ºÎºÐÁýÇÕÀ» °è»êÇÏ´Â ½Ã°£À» ÁÙÀ̱â À§ÇØ ¼±Çü Ž»ö¿¡ ±â¹ÝÇÑ ´Ü¸»³ëµå °¡ÁöÄ¡±â ±â¹ý°ú ±Ù»ç ¾Ë°í¸®Áò ±â¹ý ¹× ÀÓ°è°ª¿¡ ±â¹ÝÇÑ °¡ÁöÄ¡±â ±â¹ýµéÀ» Á¦¾ÈÇÑ´Ù. |
¿µ¹®³»¿ë (English Abstract) |
Collective spatial keyword queries return Points of Interest (POI), which are close to the query location and contain all the presented set of keywords. However, existing studies only consider a fixed number of query keywords, which is not adequate to satisfy the user. They do not care about the preference of a partial keyword set, and a flexible keyword set needs to be selected for the preference of each POI. We thus propose a new query, called Partially Collective Spatial Keyword Query, which flexibly considers keywords that fit the preference for each POI. Since this query is a combinatorial optimization problem, the query processing time increases rapidly as the number of POIs increases. Therefore, to address these problems, we propose a keyword-based search technique that reduces the overall search space. Furthermore, we propose heuristic techniques, which include the linear search-based terminal node pruning technique, approximation algorithm, and threshold-based pruning technique. |
Å°¿öµå(Keyword) |
ÁúÀÇ Ã³¸®
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°ø°£ Å°¿öµå ÁúÀÇ
Áý´ÜÀû °ø°£ Å°¿öµå Áú
query processing
spatial database
spatial keyword query
collective spatial keyword query
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