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http://dx.doi.org/10.15701/kcgs.2016.22.4.21

Fast Visualization Technique and Visual Analytics System for Real-time Analyzing Stream Data  

Jeong, Seongmin (Sejong University, Computer Engineering)
Yeon, Hanbyul (Sejong University, Computer Engineering)
Jeong, Daekyo (Sejong University, Computer Engineering)
Yoo, Sangbong (Sejong University, Computer Engineering)
Kim, Seokyeon (Sejong University, Computer Engineering)
Jang, Yun (Sejong University, Computer Engineering)
Publication Information
Journal of the Korea Computer Graphics Society / v.22, no.4, 2016 , pp. 21-30 More about this Journal
Abstract
Risk management system should be able to support a decision making within a short time to analyze stream data in real time. Many analytical systems consist of CPU computation and disk based database. However, it is more problematic when existing system analyzes stream data in real time. Stream data has various production periods from 1ms to 1 hour, 1day. One sensor generates small data but tens of thousands sensors generate huge amount of data. If hundreds of thousands sensors generate 1GB data per second, CPU based system cannot analyze the data in real time. For this reason, it requires fast processing speed and scalability for analyze stream data. In this paper, we present a fast visualization technique that consists of hybrid database and GPU computation. In order to evaluate our technique, we demonstrate a visual analytics system that analyzes pipeline leak using sensor and tweet data.
Keywords
Visual Analytics; Real-time analysis; Fast Visualization; Stream Data;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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