A new and efficient algorithm to look for periodic patterns on spatio-temporal databases

被引:2
|
作者
Gutierrez-Soto, Claudio [1 ]
Gutierrez-Bunster, Tatiana [1 ]
Fuentes, Guillermo [1 ]
机构
[1] Univ Bio Bio, Dept Sistemas Informac, Ave Collao, Concepcion, Chile
关键词
Pattern searching; Association rule algorithms; spatio-temporal databases; THINGS APPLICATIONS; INTERNET;
D O I
10.3233/JIFS-219245
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Big Data is a generic term that involves the storing and processing of a large amount of data. This large amount of data has been promoted by technologies such as mobile applications, Internet of Things (IoT), and Geographic Information Systems (GIS). An example of GIS is a Spatio-Temporal Database (STDB). A complex problem to address in terms of processing time is pattern searching on STDB. Nowadays, high information processing capacity is available everywhere. Nevertheless, the pattern searching problem on STDB using traditional Data Mining techniques is complex because the data incorporate the temporal aspect. Traditional techniques of pattern searching, such as time series, do not incorporate the spatial aspect. For this reason, traditional algorithms based on association rules must be adapted to find these patterns. Most of the algorithms take exponential processing times. In this paper, a new efficient algorithm (named Minus-F1) to look for periodic patterns on STDB is presented. Our algorithm is compared with Apriori, Max-Subpattern, and PPA algorithms on synthetic and real STDB. Additionally, the computational complexities for each algorithm in the worst cases are presented. Empirical results show that Minus-F1 is not only more efficient than Apriori, Max-Subpattern, and PAA, but also it presents a polynomial behavior.
引用
收藏
页码:4563 / 4572
页数:10
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