A Framework for Discovering Frequent Event Graphs from Uncertain Event-based Spatio-temporal Data

Abstract

The aim of this paper is to discuss a novel framework designed for discovering frequent event graphs from uncertain spatio-temporal data. We consider the problem of discovering hidden relations between event types and their set of uncertain spatio-temporal instances. For that purpose, we designed the following data mining framework: microclustering of uncertain instances, generating set of possible worlds according to the possible worlds semantic technique, creating a microclustering index for each world, generating a set of event graphs from created microclusters and defining apriori based algorithm mining frequent event graphs (EventGraph Miner). To the best of our knowledge this is the first approach to discover hidden patterns from event-type spatio-temporal data when dataset contains uncertain instances. While the paper does not present experimental results for the proposed framework, it presents its potential for futher studies in the topic.