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Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
Åýà µ¥ÀÌÅÍ¿¡ ´ëÇÑ È¿À²ÀûÀÎ Top-K ºóµµ °Ë»ö |
¿µ¹®Á¦¸ñ(English Title) |
Finding Frequent Route of Taxi Trip Events Based on MapReduce and MongoDB |
ÀúÀÚ(Author) |
Fadhilah Kurnia Putri
Seonga An
Magdalena Trie Purnaningtyas
Han-You Jeong
Joonho Kwon
Fadhilah Kurnia Putri
¾È¼º¾Æ
Magdalena Trie Purnaningtyas
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±ÇÁØÈ£
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¿ø¹®¼ö·Ïó(Citation) |
VOL 04 NO. 09 PP. 0347 ~ 0356 (2015. 09) |
Çѱ۳»¿ë (Korean Abstract) |
IoT(»ç¹°ÀÎÅͳÝ) ±â¼úÀÇ ºü¸¥ °³¹ß·Î ÀÎÇÏ¿© ±âÁ¸ÀÇ ÅýõéÀº µð½ºÆÐó¿Í À§Ä¡½Ã½ºÅÛÀ» ÅëÇØ ¼·Î ¿¬°áµÇ°í ÀÖ´Ù. ÀϹÝÀûÀ¸·Î Çö´ëÀÇ ÅýõéÀº °æ·Î Á¤º¸¸¦ ȹµæÇϱâ À§ÇÑ ¸ñÀûÀ¸·Î GPS(Global Positioning System)¸¦ žÀçÇÏ°í ÀÖ´Ù. Åýà ¿îÇà µ¥ÀÌÅ͵éÀÇ °æ·Î ºóµµ¸¦ ºÐ¼®ÇÏ¿© ÁÖ¾îÁø ÁúÀÇ ½Ã°£¿¡ ÇØ´çÇÏ´Â ºó¹øÇÑ °æ·Î¸¦ ãÀ» ¼ö ÀÖ´Ù. ±×·¯³ª À§Ä¡ µ¥ÀÌÅÍÀÇ ¿ë·®ÀÌ ¸Å¿ì Å©°í º¹ÀâÇϱ⠶§¹®¿¡ ÅýÃÀÇ ¿îÇà À̺¥Æ®ÀÇ À§Ä¡ µ¥ÀÌÅ͸¦ ºÐ¼®µÈ ºóµµ Á¤º¸·Î º¯È¯ÇÒ ¶§¿¡ È®À强 ¹®Á¦°¡ ¹ß»ýÇÑ´Ù. ÀÌ ¹®Á¦¸¦ ÇØ°áÇϱâ À§ÇÏ¿© NoSQL µ¥ÀÌÅͺ£À̽º¿¡ ±â¹ÝÇÑ Åýà ¿îÇà µ¥ÀÌÅÍ¿¡ ´ëÇÑ Top-K ÁúÀÇ ½Ã½ºÅÛÀ» Á¦¾ÈÇÑ´Ù. ù°, ¿ø½Ã Åýà ¿îÇà À̺¥Æ®¸¦ ºÐ¼®ÇÏ°í ¸ðµç °æ·ÎµéÀÇ ºóµµ Á¤º¸¸¦ ÃßÃâÇÑ´Ù. ÃßÃâÇÑ °æ·Î Á¤º¸´Â NoSQL ¹®¼-ÁöÇâ µ¥ÀÌÅͺ£À̽ºÀÎ MongoDB¿¡ Çؽà ±â¹ÝÀÇ À妽º ±¸Á¶·Î ÀúÀåÇÑ´Ù. ÁÖ·Î ¹ß»ýÇÏ´Â °æ·Î¿¡ ´ëÇÑ È¿À²ÀûÀÎ Top-K ÁúÀÇ󸮴 ¸ù°íDBÀÇ »ó¿¡¼ ÀÌ·ç¾îÁø´Ù. ¹Ì±¹ ´º¿å½ÃÀÇ ½ÇÁ¦ Åýà ¿îÇà µ¥ÀÌÅ͸¦ ÀÌ¿ëÇÑ ½ÇÇèÀ» ÅëÇÏ¿© ¾Ë°í¸®ÁòÀÇ È¿À²¼ºÀ» °ËÁõÇÏ¿´´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Due to the rapid development of IoT(Internet of Things) technology, traditional taxis are connected through dispatchers and location systems. Typically, modern taxis have embedded with GPS(Global Positioning System), which aims for obtaining the route information. By analyzing the frequency of taxi trip events, we can find the frequent route for a given query time. However, a scalability problem would occur when we convert the raw location data of taxi trip events into the analyzed frequency information due to the volume of location data. For this problem, we propose a NoSQL based top-K query system for taxi trip events. First, we analyze raw taxi trip events and extract frequencies of all routes. Then, we store the frequency information into hash-based index structure of MongoDB which is a document-oriented NoSQL database. Efficient top-K query processing for frequent route is done with the top of the MongoDB. We validate the efficiency of our algorithms by using real taxi trip events of New York City.
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Å°¿öµå(Keyword) |
Åýà ¿îÇà µ¥ÀÌÅÍ
Top-K ÁúÀÇ Ã³¸®
NoSQL µ¥ÀÌÅͺ£À̽º
MapReduce
MongoDB
Taxi Trip Data
Top-K Frequent Query Processing
NoSQL Database
MapReduce
MongoDB
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