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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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KIPS Transactions on Software and Data Engineering
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Korea Information Processing Society
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Volume & Issues
Volume 5, Issue 8 - Aug 2016
Volume 5, Issue 7 - Jul 2016
Volume 5, Issue 6 - Jun 2016
Volume 5, Issue 5 - May 2016
Volume 5, Issue 4 - Apr 2016
Volume 5, Issue 3 - Mar 2016
Volume 5, Issue 2 - Feb 2016
Volume 5, Issue 1 - Jan 2016
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A Guiding System of Visualization for Quantitative Bigdata Based on User Intention
Byun, Jung Yun ; Park, Young B. ;
KIPS Transactions on Software and Data Engineering, volume 5, issue 6, 2016, Pages 261~266
DOI : 10.3745/KTSDE.2016.5.6.261
Chart suggestion method provided by various existing data visualization tools makes chart recommendations without considering the user intention. Data visualization is not properly carried out and thus, unclear in some tools because they do not follow the segmented quantitative data classification policy. This paper provides a guideline that clearly classifies the quantitative input data and that effectively suggests charts based on user intention. The guideline is two-fold; the analysis guideline examines the quantitative data and the suggestion guideline recommends charts based on the input data type and the user intention. Following this guideline, we excluded charts in disagreement with the user intention and confirmed that the time user spends in the chart selection process has decreased.
Development of HCS(High Contents Screening) Software Using Open Source Library
Na, Ye Ji ; Ho, Jong Gab ; Lee, Sang Joon ; Min, Se Dong ;
KIPS Transactions on Software and Data Engineering, volume 5, issue 6, 2016, Pages 267~272
DOI : 10.3745/KTSDE.2016.5.6.267
Microscope cell image is an important indicator for obtaining the biological information in a bio-informatics fields. Since human observers have been examining the cell image with microscope, a lot of time and high concentration are required to analyze cell images. Furthermore, It is difficult for the human eye to quantify objectively features in cell images. In this study, we developed HCS algorithm for automatic analysis of cell image using an OpenCV library. HCS algorithm contains the cell image preprocessing, cell counting, cell cycle and mitotic index analysis algorithm. We used human cancer cell (MKN-28) obtained by the confocal laser microscope for image analysis. We compare the value of cell counting to imageJ and to a professional observer to evaluate our algorithm performance. The experimental results showed that the average accuracy of our algorithm is 99.7%.
GLOVE: Distributed Shared Memory Based Parallel Visualization Tool for Massive Scientific Dataset
Lee, Joong-Youn ; Kim, Min Ah ; Lee, Sehoon ; Hur, Young Ju ;
KIPS Transactions on Software and Data Engineering, volume 5, issue 6, 2016, Pages 273~282
DOI : 10.3745/KTSDE.2016.5.6.273
Visualization tool can be divided by three components - data I/O, visual transformation and interactive rendering. In this paper, we present requirements of three major components on visualization tools for massive scientific dataset and propose strategies to develop the tool which satisfies those requirements. In particular, we present how to utilize open source softwares to efficiently realize our goal. Furthermore, we also study the way to combine several open source softwares which are separately made to produce a single visualization software and optimize it for realtime visualization of massiv espatio-temporal scientific dataset. Finally, we propose a distributed shared memory based scientific visualization tool which is called "GLOVE". We present a performance comparison among GLOVE and well known open source visualization tools such as ParaView and VisIt.
Anomaly Detection of Hadoop Log Data Using Moving Average and 3-Sigma
Son, Siwoon ; Gil, Myeong-Seon ; Moon, Yang-Sae ; Won, Hee-Sun ;
KIPS Transactions on Software and Data Engineering, volume 5, issue 6, 2016, Pages 283~288
DOI : 10.3745/KTSDE.2016.5.6.283
In recent years, there have been many research efforts on Big Data, and many companies developed a variety of relevant products. Accordingly, we are able to store and analyze a large volume of log data, which have been difficult to be handled in the traditional computing environment. To handle a large volume of log data, which rapidly occur in multiple servers, in this paper we design a new data storage architecture to efficiently analyze those big log data through Apache Hive. We then design and implement anomaly detection methods, which identify abnormal status of servers from log data, based on moving average and 3-sigma techniques. We also show effectiveness of the proposed detection methods by demonstrating that our methods identifies anomalies correctly. These results show that our anomaly detection is an excellent approach for properly detecting anomalies from Hadoop log data.
A Personalized Dietary Coaching Method Using Food Clustering Analysis
Oh, Yoori ; Choi, Jieun ; Kim, Yoonhee ;
KIPS Transactions on Software and Data Engineering, volume 5, issue 6, 2016, Pages 289~294
DOI : 10.3745/KTSDE.2016.5.6.289
In recent times, as most people develop keen interest in health management, the importance of cultivating dietary habits to prevent various chronic diseases is emphasized. Subsequently, dietary management systems using a variety of mobile and web application interfaces have emerged. However, these systems are difficult to apply in real world and also do not provide personalized information reflective of the user`s situation. Hence it is necessary to develop a personalized dietary management and recommendation method that considers user`s body state information, food analysis and other essential statistics. In this paper, we analyze nutrition using self-organizing map (SOM) and prepare data about nutrition using clustering. We provide a substitute food recommendation method and also give feedback about the food that user wants to eat based on personalized criteria. The experiment results show that the distance between input food and recommended food of the proposed method is short compared to the recommended food results using general methods and proved that nutritional similar food is recommended.
Trend of Research and Industry-Related Analysis in Data Quality Using Time Series Network Analysis
Jang, Kyoung-Ae ; Lee, Kwang-Suk ; Kim, Woo-Je ;
KIPS Transactions on Software and Data Engineering, volume 5, issue 6, 2016, Pages 295~306
DOI : 10.3745/KTSDE.2016.5.6.295
The purpose of this paper is both to analyze research trends and to predict industrial flows using the meta-data from the previous studies on data quality. There have been many attempts to analyze the research trends in various fields till lately. However, analysis of previous studies on data quality has produced poor results because of its vast scope and data. Therefore, in this paper, we used a text mining, social network analysis for time series network analysis to analyze the vast scope and data of data quality collected from a Web of Science index database of papers published in the international data quality-field journals for 10 years. The analysis results are as follows: Decreases in Mathematical & Computational Biology, Chemistry, Health Care Sciences & Services, Biochemistry & Molecular Biology, Biochemistry & Molecular Biology, and Medical Information Science. Increases, on the contrary, in Environmental Sciences, Water Resources, Geology, and Instruments & Instrumentation. In addition, the social network analysis results show that the subjects which have the high centrality are analysis, algorithm, and network, and also, image, model, sensor, and optimization are increasing subjects in the data quality field. Furthermore, the industrial connection analysis result on data quality shows that there is high correlation between technique, industry, health, infrastructure, and customer service. And it predicted that the Environmental Sciences, Biotechnology, and Health Industry will be continuously developed. This paper will be useful for people, not only who are in the data quality industry field, but also the researchers who analyze research patterns and find out the industry connection on data quality.
Feature Point Filtering Method Based on CS-RANSAC for Efficient Planar Homography Estimating
Kim, Dae-Woo ; Yoon, Ui-Nyoung ; Jo, Geun-Sik ;
KIPS Transactions on Software and Data Engineering, volume 5, issue 6, 2016, Pages 307~312
DOI : 10.3745/KTSDE.2016.5.6.307
Markerless tracking for augmented reality using Homography can augment virtual objects correctly and naturally on live view of real-world environment by using correct pose and direction of camera. The RANSAC algorithm is widely used for estimating Homography. CS-RANSAC algorithm is one of the novel algorithm which cooperates a constraint satisfaction problem(CSP) into RANSAC algorithm for increasing accuracy and decreasing processing time. However, CS-RANSAC algorithm can be degraded performance of calculating Homography that is caused by selecting feature points which estimate low accuracy Homography in the sampling step. In this paper, we propose feature point filtering method based on CS-RANSAC for efficient planar Homography estimating the proposed algorithm evaluate which feature points estimate high accuracy Homography for removing unnecessary feature point from the next sampling step using Symmetric Transfer Error to increase accuracy and decrease processing time. To evaluate our proposed method we have compared our algorithm with the bagic CS-RANSAC algorithm, and basic RANSAC algorithm in terms of processing time, error rate(Symmetric Transfer Error), and inlier rate. The experiment shows that the proposed method produces 5% decrease in processing time, 14% decrease in Symmetric Transfer Error, and higher accurate homography by comparing the basic CS-RANSAC algorithm.