🤖 AI Summary
This study addresses the challenges of redundancy and limited interpretability in mining frequent interaction patterns from spatiotemporal event data. To this end, it proposes modeling events as labeled nodes and representing their spatiotemporal precedence relationships through directed acyclic graphs (DAGs). The work introduces, for the first time, frequent closed embedded sub-DAGs as a compact, non-redundant, and semantically meaningful representation of such patterns. The authors design and implement the DigDag algorithm to efficiently mine these substructures. Experimental results demonstrate that, under identical parameter settings, DigDag significantly outperforms SLEUTH and CSTPM in computational efficiency, while the discovered patterns exhibit clear practical relevance in qualitative analysis.
📝 Abstract
We propose a novel approach to mine patterns in spatio-temporal event data based on discovering frequent closed embedded sub-Directed Acyclic Graphs (DAGs). In our method, event instances are represented as nodes labelled by event types, while edges capture spatio-temporal following relationships. We formally define the considered class of patterns and provide the rationale for focusing on closed sub-DAGs as compact and non-redundant representations of recurring interaction patterns. We implement the DigDag algorithm for mining such patterns and experimentally compare its efficiency with two related approaches: propagation pattern mining using the SLEUTH algorithm and Cascading Spatio-Temporal Pattern mining using the CSTPM algorithm. The experimental results demonstrate that our approach is substantially more efficient while operating under comparable parameter settings. Finally, we present a qualitative analysis of selected discovered patterns.