Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

被引:12
|
作者
Ravikumar, Penugonda [1 ,2 ]
Likhitha, Palla [2 ]
Raj, Bathala Venus Vikranth [2 ]
Kiran, Rage Uday [1 ,3 ]
Watanobe, Yutaka [1 ]
Zettsu, Koji [3 ]
机构
[1] Univ AIZU, Dept Comp & Informat Syst, Aizu Wakamatsu, Fukushima 9658580, Japan
[2] RGUKT Andhara Pradesh, IIIT RK Valley, Nuzividu 521202, Andhra Pradesh, India
[3] NICT, Yokosuka, Kanagawa 2390847, Japan
关键词
data mining; pattern mining; periodic-frequent patterns; columnar databases;
D O I
10.3390/electronics10121478
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network.
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页数:20
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