Open AccessPARALLEL IMAGE DATABASE PROCESSING WITH MAPREDUCE AND PERFORMANCE EVALUATION IN PSEUDO DISTRIBUTED MODE
Author(s) -
Muneto Yamamoto
Publication year - 2012
Publication title -
international journal of electronic commerce studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.196
H-Index - 9
eISSN - 2410-8588
pISSN - 2073-9729
DOI - 10.7903/ijecs.1092
Subject(s) - computer science , cloud computing , database , data intensive computing , process (computing) , distributed database , big data , mode (computer interface) , parallel processing , data processing , image processing , resource (disambiguation) , distributed computing , image (mathematics) , parallel computing , data mining , operating system , artificial intelligence , computer network , grid computing , geometry , mathematics , grid
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