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JaeHong KIM
YoungDong University

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A New Memory MapReduce Framework for Higher Access to Resources ZuKuan WEI; Bo HONG; JaeHong KIM
Indonesian Journal of Electrical Engineering and Computer Science Vol 4, No 3: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v4.i3.pp629-636

Abstract

The demand for highly parallel data processing platform was growing due to an explosion in the number of massive-scale data applications both in academia and industry. MapReduce was one of the most meaningful solutions to deal with big data distributed computing, This paper was based on the work of Hadoop MapReduce. In the face of massive data computing and calculation process, MapReduce generated a lot of dynamic data, but these data were discarded after the task completed. Meanwhile, a large number of dynamic data were written to HDFS during task execution, caused much unnecessary IO cost. In this paper, we analyzed existing distributed caching mechanism and proposed a new Memory MapReduce framework that has a real-time response to read or write request from task nodes, maintain related information about cache data. After performance testing, we could clearly find MapReduce with cache significantly improved in IO performance.
An Efficient Content Based Image Retrieval Scheme Zukuan WEI; Hongyeon KIM; Youngkyun KIM; Jaehong KIM
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 11: November 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Due to the recent improvements in digital photography and storage capacity, storing large amounts of images has been made possible. Consequently efficient means to retrieve images matching a user’s query are needed. In this paper, we propose a framework based on a bipartite graph model (BGM) for semantic image retrieval. BGM is a scalable data structure that aids semantic indexing in an efficient manner, and it can also be incrementally updated. Firstly, all the images are segmented into several regions with image segmentation algorithm, pre-trained SVMs are used to annotate each region, and final label is obtained by merging all the region labels. Then we use the set of images and the set of region labels to build a bipartite graph. When a query is given, a query node, initially containing a fixed number of labels, is created to attach to the bipartite graph. The node then distributes the labels based on the edge weight between the node and its neighbors. Image nodes receiving the most labels represent the most relevant images. Experimental results demonstrate that our proposed technique is promising.DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3588
Co-Authors Bo HONG Hongyeon KIM Youngkyun KIM Zukuan WEI ZuKuan WEI