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

Abstract

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.

An Efficient Probabilistic Algorithm to Detect Periodic Patterns in Spatio-Temporal Datasets

“…Thus, we have conducted a comprehensive comparison of these algorithms over two STDBs-first, a synthetic one, then a real one. As part of our experiments, we have also included the Minus-F1 algorithm [13], which has been proven to achieve good results, and a new probabilistic version of it, which we have developed.…”

“…An efficient probabilistic algorithm: Although recent developments have produced several off-the-shelf libraries for pattern mining-for instance, apyori [14] is a library that implements the Apriori algorithm in Python-our experiments have confirmed that the performance of the most well-known algorithms is not ideal for STDBs. Thus, we have developed a new, probabilistic version of the Minus-F1 algorithm [13], which we refer to as F1/FP. This new algorithm allows for periodic pattern discovery in STDBs.…”

“…Methods based on mathematics are also robust against noise and efficient at extracting partial periodic patterns, without additional domain knowledge. Regrettably, they prioritize computational efficiency by employing approximations, which may miss some periodic patterns [13]. In other words, mathematical methods trade off the guarantee of finding all the qualifying patterns for faster execution times.…”

“…Spatio-temporal databases are another area which extends the scope of the problem with many new applications, such as disease diffusion analysis [26], user activity analysis [27], and local trend discovery in social networks [28,29]. Several approaches have been proposed to deal with spatial information [30], treating it as a continuous variable [31,32], formulating it as a dynamic graph mining problem [33], and encoding spatial features as discrete symbols [13]. We have adopted the discrete symbol encoding approach to fully exploit our former research on sequential periodic pattern mining [13].…”

“…Several approaches have been proposed to deal with spatial information [30], treating it as a continuous variable [31,32], formulating it as a dynamic graph mining problem [33], and encoding spatial features as discrete symbols [13]. We have adopted the discrete symbol encoding approach to fully exploit our former research on sequential periodic pattern mining [13].…”