PARALLEL IMAGE DATABASE PROCESSING WITH MAPREDUCE AND PERFORMANCE EVALUATION IN PSEUDO DISTRIBUTED MODE

With recent improvements in camera performance and the spread of low-priced and lightweight video cameras, a large amount of video data is generated, and stored in database form. At the same time, there are limits on what can be done to improve the performance of single computers to make them able to process large-scale information, such as in video analysis. Therefore, an important research topic is how to perform parallel distributed processing of a video database by using the computational resource in a cloud environment. At present, the Apache Hadoop distribution for open-source cloud computing is available from MapReduce. In the present study, we report our results on an evaluation of performance, which remains a problem for video processing in distributed environments, and on parallel experiments using MapReduce on Hadoop. To cite this document: Muneto Yamamoto and Kunihiko Kaneko, "Parallel image database processing with mapreduce and performance evaluation in pseudo distributed mode", International Journal of Electronic Commerce Studies, Vol.3, No.2, pp.211-228, 2012. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1092