Discovering Fuzzy Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases
Pamalla Veena, Penugonda Ravikumar, Kundai Kwangwari, Rage Uday Kiran, Kazuo Goda, Yutaka Watanobe, Koji Zettsu · 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · 2022
A geo-referenced time series database represents the data generated by a set of fixed locations (or items) observing a particular phenomenon over time. Useful information that can facilitate the users to achieve socio-economic development lies hidden in this data. This paper introduces a novel model of Fuzzy Geo-referenced Periodic-Frequent Patterns (FGPFPs) that may exist in these databases. An FGPFP represents a set of frequently occurring neighboring items observed at regular intervals in a database. For example, an FGPFP in a traffic congestion database represents a set of neighboring road segments where people have regularly faced congestion problems. A novel pruning technique has been presented to effectively reduce the search space and the computational cost of finding the desired patterns. We have also proposed an efficient depth-first search algorithm to find all the desired patterns. Experimental results demonstrate that the proposed algorithm is efficient. Finally, we demonstrate our model’s usefulness by performing traffic congestion analytics.