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http://dx.doi.org/10.5392/JKCA.2018.18.02.147

Incremental Frequent Pattern Detection Scheme Based on Sliding Windows in Graph Streams  

Jeong, Jaeyun (충북대학교 정보통신공학과)
Seo, Indeok (충북대학교 빅데이터학과)
Song, Heesub (충북대학교 빅데이터학과)
Park, Jaeyeol (충북대학교 정보통신공학과)
Kim, Minyeong (충북대학교 정보통신공학과)
Choi, Dojin (충북대학교 정보통신공학과)
Bok, Kyoungsoo (충북대학교 정보통신공학과)
Yoo, Jaesoo (충북대학교 정보통신공학과)
Publication Information
The Journal of the Korea Contents Association / v.18, no.2, 2018 , pp. 147-157 More about this Journal
Abstract
Recently, with the advancement of network technologies, and the activation of IoT and social network services, many graph stream data have been generated. As the relationship between objects in the graph streams changes dynamically, studies have been conducting to detect or analyze the change of the graph. In this paper, we propose a scheme to incrementally detect frequent patterns by using frequent patterns information detected in previous sliding windows. The proposed scheme calculates values that represent whether the frequent patterns detected in previous sliding windows will be frequent in how many future silding windows. By using the values, the proposed scheme reduces the overall amount of computation by performing only necessary calculations in the next sliding window. In addition, only the patterns that are connected between the patterns are recognized as one pattern, so that only the more significant patterns are detected. We conduct various performance evaluations in order to show the superiority of the proposed scheme. The proposed scheme is faster than existing similar scheme when the number of duplicated data is large.
Keywords
Graph Analysis; Graph Stream; Frequent Pattern Detection; Incremental Processing; Sliding Window;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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