• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.670-686
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• 2017

MapReduce (MRV1), a popular programming model, proposed by Google, has been well used to process large datasets in Hadoop, an open source cloud platform. Its new version MapReduce 2.0 (MRV2) developed along with the emerging of Yarn has achieved obvious improvement over MRV1. However, MRV2 suffers from long finishing time on certain types of jobs. Speculative Execution (SE) has been presented as an approach to the problem above by backing up those delayed jobs from low-performance machines to higher ones. In this paper, an adaptive SE strategy (ASE) is presented in Hadoop-2.6.0. Experiment results have depicted that the ASE duplicates tasks according to real-time resources usage among work nodes in a cloud. In addition, the performance of MRV2 is largely improved using the ASE strategy on job execution time and resource consumption, whether in a multi-job environment.

• Journal of Information Technology and Architecture
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• v.10 no.3
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• pp.387-397
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• 2013

This paper proposes an approach to big data analytics for phon call data. The analytical models for phon call data is composed of the PVPF (Parallel Variable-length Phrase Finding) algorithm for identifying verbal phrases of natural language and the word count algorithm for measuring the usage frequency of keywords. In the proposed model, we identify words using the PVPF algorithm, and measure the usage frequency of the identified words using word count algorithm in MapReduce. The results can be interpreted from various viewpoints. We design and implement the model based HDFS (Hadoop Distributed File System), verify the proposed approach through a case study of phon call data. So we extract useful results through analysis of keyword correlation and usage frequency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.576-585
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• 2014

Collecting and utilizing have a huge amount of personal data have caused severe security issues such as leakage of personal information. Several encryption algorithms for collected personal information have been widely adopted to prevent such problems. In this paper, a novel algorithm based on MapReduce is proposed for encrypting such private information. Furthermore, test environment has been built for the performance verification of the distributed encryption processing method. As the result of the test, average time efficiency has improved to 15.3% compare to encryption processing of token server and 3.13% compare to parallel processing.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.179-187
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• 2011

Multiple roadmap DB providers are already available in these days, and try to reduce unknown roads in their own roadmaps. However, cooperation models or Win-Win approaches between roadmap providers are not considered yet. Thus, In this paper, We proposed a cloud-oriented real-time roadmap generation and update method between heterogeneous navigation systems for unknown roads. With the proposed method, the roadmap DB providers update the own roadmap DB for navigation systems in real time. Also, they can provide the complete roadmap without unknown roads to users instantly. Therefore, the proposed method can reduce the costs of an actual traveling test and the maintenance for the roadmap DB provides. Thus, the cloud-oriented roadmap generation method can more efficiently update the unknown road information.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.86-89
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• 2002

In NC machining, the ability to automatically generate an optimal process plan is an essential step toward achieving automation, higher productivity, and better accuracy. For this ability, a system that is capable of simulating the actual machining process has to be designed. In this paper, a milling process simulation system for the general NC machining was presented. The system needs first to accurately compute the cutting configuration. ME Z-map(Moving Edge node Z-map) was developed to reduce the entry/exit angle calculation error in cutting force prediction. It was shorn to drastically improve the conventional Z-map model. Experimental results applied to the pocket machining show the accuracy of the milling process simulation system.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.275-276
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• 2008

IEEE802.16e-based OFDMA system called WiBro is being serviced commercially. In WiBro system, the base station sends downlink(DL)-MAP information to all mobile stations in each cell. The DL-MAP information is repeated six times, modulated by QPSK, and coded by Convolutional Turbo Coding(CTC) with 1/2 code rate [1],[2]. As the number of mobile stations increases, the DL-MAP size also increases. In this paper, We investigate methods of power allocation and interference cancelation to reduce overhead of the DL-MAP.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.558-568
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• 2000

In the paper, we propose the automatic generation method of digital elevation map based on contour map. The proposed method classifies contour data and non-contour data by thinning and labelling process of the contour line and then connects smoothly broken contour line by Bezier curve. Finally, the digital devation map is generated by the interpolation using the height data of the contour line. The proposed method can reduce vest effort, time and expense which is spend to make digital elevation map.

• Journal of Communications and Networks
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• v.6 no.3
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• pp.209-215
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• 2004

In this paper, a butterfly Log-MAP decoding algorithm for turbo code is proposed. Different from the conventional turbo decoder, we derived a generalized formula to calculate the log-likelihood ratio (LLR) and drew a modified butterfly states diagram in 8-states systematic turbo coded system. By comparing the complexity of conventional implementations, the proposed algorithm can efficiently reduce both the computations and work units without bit error ratio (BER) performance degradation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.06a
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• pp.131-136
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• 1997

We present a method to estimate a dense and sharp depth map using multiple cameras for the application to flexible video production. A key issue for obtaining sharp depth map is how to overcome the harmful influence of occlusion. Thus, we first propose to selectively use the depth information from multiple cameras. With a simple sort and discard technique, we resolve the occlusion problem considerably at a slight sacrifice of noise tolerance. However, boundary overreach of more textured area to less textured area at object boundaries still remains to be solved. We observed that the amount of boundary overreach is less than half the size of the matching window and, unlike usual stereo matching, the boundary overreach with the proposed occlusion-overcoming method shows very abrupt transition. Based on these observations, we propose a hierarchical estimation scheme that attempts to reduce boundary overreach such that edges of the depth map coincide with object boundaries on the one hand, and to reduce noisy estimates due to insufficient size of matching window on the other hand. We show the hierarchical method can produce a sharp depth map for a variety of images.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.285-304
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• 2017

Big data information and pattern analysis have applications in many industrial sectors. To reduce energy consumption effectively, the eco-driving method that reduces the fuel consumption of vehicles has recently come under scrutiny. Using big data on commercial vehicles obtained from digital tachographs (DTGs), it is possible not only to aid traffic safety but also improve eco-driving. In this study, we estimate fuel consumption efficiency by processing and analyzing DTG big data for commercial vehicles using parallel processing with the MapReduce mechanism. Compared to the conventional measurement of fuel consumption using the On-Board Diagnostics II (OBD-II) device, in this paper, we use actual DTG data and OBD-II fuel consumption data to identify meaningful relationships to calculate fuel efficiency rates. Based on the driving pattern extracted from DTG data, estimating fuel consumption is possible by analyzing driving patterns obtained only from DTG big data.