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http://dx.doi.org/10.12672/ksis.2012.20.2.137

Update Frequency Reducing Method of Spatio-Temporal Big Data based on MapReduce  

Choi, Youn-Gwon (인하대학교 정보공학과)
Baek, Sung-Ha (인하대학교 컴퓨터 정보공학과)
Kim, Gyung-Bae (서원대학교 컴퓨터교육과)
Bae, Hae-Young (중국 중경우전대학교 대학원)
Publication Information
Spatial Information Research / v.20, no.2, 2012 , pp. 137-153 More about this Journal
Abstract
Until now, many indexing methods that can reduce update cost have been proposed for managing massive moving objects. Because indexing methods for moving objects have to be updated periodically for managing moving objects that change their location data frequently. However these kinds indexing methods occur big load that exceed system capacity when the number of moving objects increase dramatically. In this paper, we propose the update frequency reducing method to combine MapReduce and existing indices. We use the update request grouping method for each moving object by using MapReduce. We decide to update by comparing the latest data and the oldest data in grouping data. We reduce update frequency by updating the latest data only. When update is delayed, for the data should not be lost and updated periodically, we store the data in a certain period of time in the hash table that keep previous update data. By the performance evaluation, we can prove that the proposed method reduces the update frequency by comparison with methods that are not applied the proposed method.
Keywords
Big Data; Spatial-Demporal Data; LBS; Index; Cloud System; Hadoop;
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