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http://dx.doi.org/10.3745/KTSDE.2015.4.9.347

Finding Frequent Route of Taxi Trip Events Based on MapReduce and MongoDB  

Putri, Fadhilah Kurnia (부산대학교 빅데이터협동과정)
An, Seonga (부산대학교 빅데이터협동과정)
Purnaningtyas, Magdalena Trie (부산대학교 전기컴퓨터공학과)
Jeong, Han-You (부산대학교 전기공학과)
Kwon, Joonho (부산대학교 빅데이터협동과정)
Publication Information
KIPS Transactions on Software and Data Engineering / v.4, no.9, 2015 , pp. 347-356 More about this Journal
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.
Keywords
Taxi Trip Data; Top-K Frequent Query Processing; NoSQL Database; MapReduce; MongoDB;
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