Discovering Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases
Penugonda Ravikumar, Rage Uday Kiran, Palla Likhitha, T. Chandrasekhar, Yutaka Watanobe, Koji Zettsu · 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) · 2022
A geo-referenced time series database represents the data generated by a set of fixed locations (or spatial items) observing a particular phenomenon over time. This data hides valuable information that can help users progress in their social and economic lives. This paper presents a new model of Geo-referenced Periodic-Frequent Patterns (GPFPs) that might be in these databases. A GPFP is a set of frequently occurring items close to each other and seen in a database at regular intervals. Three constraints have been used to figure out how interesting a pattern is in a geo-referenced time series database: maximum distance (maxDist), minimum support (minSup), and maximum periodicity (maxPer). The maxDist controls how far apart the items in a pattern can be. The minimum number of times a pattern must appear in the data is controlled by the minSup. Lastly, the maxPer variable specifies how many times a pattern must repeat before it is considered periodic in the data. Each pattern that satisfies these three requirements will be returned. An effective method known as the Geo-referenced Periodic-Frequent Pattern-Miner (GPFP-Miner) has been proposed to discover all GPFPs included inside a geo-referenced time series database. GPFP-Miner uses an innovative, smart depth-first search approach to uncover required patterns efficiently. The findings of the experiments support the contention that the proposed algorithm is effective. In addition, we present two case studies in which we utilise our methodology to extract meaningful information from databases pertaining to air pollution and traffic congestion.