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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)

Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)

Current Result Document : 3 / 7 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) ´ë¿ë·® °æ·Îµ¥ÀÌÅÍ ºÐ·ù¿¡ ±â¹ÝÇÑ °æÇèÀû ÃÖ¼± °æ·Î Ãßõ
¿µ¹®Á¦¸ñ(English Title) Recommendation of Best Empirical Route Based on Classification of Large Trajectory Data
ÀúÀÚ(Author) ÀÌ°èÇü   Á¶¿µÈÆ   ÀÌÅÂÈ£   ¹ÚÈñ¹Î   Kye Hyung Lee   Yung Hoon Jo   Tea Ho Lee   Heemin Park  
¿ø¹®¼ö·Ïó(Citation) VOL 21 NO. 02 PP. 0101 ~ 0108 (2015. 02)
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(Korean Abstract)
À§Ä¡¸¦ ¼öÁýÇÒ ¼ö ÀÖ´Â ¸ð¹ÙÀÏ ±â±âÀÇ È®»ê¿¡ µû¶ó ´Ù¾çÇÑ À§Ä¡±â¹Ý¼­ºñ½ºµéÀÌ °³¹ßµÇ¾î »ç¿ëµÇ°í ÀÖ´Ù. º» ³í¹®¿¡¼­´Â À§Ä¡±â¹Ý¼­ºñ½º°¡ ÀϹÝÈ­µÊ¿¡ µû¶ó ¼öÁýµÇ°í ÀúÀåµÇ´Â °æ·Î µ¥ÀÌÅÍÀÇ ¾çÀÌ ±âÇÏ ±Þ¼öÀûÀ¸·Î Ä¿Áö°í °á±¹ ºòµ¥ÀÌÅÍ°¡ µÉ °ÍÀ̱⠶§¹®¿¡ ¼öÁýµÈ ´ë¿ë·® °æ·Îµ¥ÀÌÅÍ¿¡¼­ ÃÖ¼± °æ·Î¸¦ ã¾Æ ÃßõÇØÁÖ´Â ½Ã½ºÅÛÀ» Á¦¾ÈÇÑ´Ù. ´ë¿ë·® °æ·Î µ¥ÀÌÅÍ¿¡¼­ ½ÇÁ¦ ¿îÇà ½Ã°£ µîÀÇ Á¤º¸¸¦ ¹ÙÅÁÀ¸·Î ±âÁ¸ ³»ºñ°ÔÀ̼Ǻ¸´Ù ÁÁÀº °æ·Î¸¦ ÃßõÇÒ ¼ö ÀÖ°Ô µÈ´Ù. ´ë¿ë·® °æ·Î µ¥ÀÌÅÍ Ã³¸®¸¦ À§ÇØ ÇÏµÓ ¸Ê¸®µà½º¸¦ ÀÌ¿ëÇؼ­ ºÐ·ùÇÏ°í ºÐ·ùµÈ °æ·Î¸¦ µ¥ÀÌÅͺ£À̽º¿¡ ÀúÀåÇÏ¿© »ç¿ëÀÚÀÇ ¿äû¿¡ ºü¸£°Ô ¹ÝÀÀÇÒ ¼ö ÀÖµµ·Ï ÇÏ¿´´Ù. »ç¿ëÀÚÀÇ ¿äû¿¡ Áöµµ»óÀÇ ÃÖ´Ü °æ·Î°¡ ¾Æ´Ñ ¼öÁýµÈ °æ·Î ±â·ÏÀ» ¹ÙÅÁÀ¸·Î ÃÖ¼± °æ·Î¸¦ ã°Ô µÇ´Â °ÍÀÌ´Ù. ±¸ÇöµÈ Àüü ½Ã½ºÅÛÀº 1) ½ÇÁ¦ °æ·Î¸¦ ¼öÁýÇϱâ À§ÇÑ ¾Èµå·ÎÀ̵å ÀÀ¿ëÇÁ·Î±×·¥, 2) ÇÏµÓ ¸Ê¸®µà½º¸¦ ÀÌ¿ëÇØ ¼öÁýµÈ °æ·Î¸¦ ¹Ì¸® ºÐ·ùÇØ ³õ±â À§ÇÑ ºÐ·ù ¿£Áø, 3) »ç¿ëÀÚÀÇ Ãâ¹ßÁö-µµÂøÁö ¿äû¿¡ µû¶ó ºÐ·ùµÈ °æ·Î¿¡¼­ ÃÖ¼± °æ·Î¸¦ ã¾Æ »ç¿ëÀÚ¿¡°Ô µ¹·ÁÁÖ´Â À¥¼­¹ö¿Í ¾Èµå·ÎÀ̵å Ŭ¶óÀ̾ðÆ® ¼­ºñ½º ½Ã½ºÅÛÀÌ´Ù. ½ÇÁ¦¿îÇà ½ÇÇèÀ» Á¦¾ÈÇÑ ¹æ¹ý°ú ½Ã½ºÅÛÀÌ ½ÇÈ¿¼ºÀÌ ÀÖÀ½À» º¸ÀδÙ.
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(English Abstract)
This paper presents the implementation of a system that recommends empirical best routes based on classification of large trajectory data. As many location-based services are used, we expect the amount of location and trajectory data to become big data. Then, we believe we can extract the best empirical routes from the large trajectory repositories. Large trajectory data is clustered into similar route groups using Hadoop MapReduce framework. Clustered route groups are stored and managed by a DBMS, and thus it supports rapid response to the end-users¡¯ request. We aim to find the best routes based on collected real data, not the ideal shortest path on maps. We have implemented 1) an Android application that collects trajectories from users, 2) Apache Hadoop MapReduce program that can cluster large trajectory data, 3) a service application to query start-destination from a web server and to display the recommended routes on mobile phones. We validated our approach using real data we collected for five days and have compared the results with commercial navigation systems. Experimental results show that the empirical best route is better than routes recommended by commercial navigation systems.
Å°¿öµå(Keyword) °æ·Îµ¥ÀÌÅÍ   ÃÖ¼±°æ·Î   ºÐ·ù   ¸Ê¸®µà½º   ³»ºñ°ÔÀ̼Ǡ  ¾Èµå·ÎÀ̵å ÀÀ¿ëÇÁ·Î±×·¥   trajectory data   best route   classification   MapReduce   navigation   android application  
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