Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data
For researchers analyzing spatio-temporal event data, this work offers a more efficient pattern mining technique, though it is incremental as it adapts existing graph mining concepts to a specific domain.
The paper introduces a method for mining frequent closed embedded sub-DAGs in spatio-temporal event data, achieving substantially higher efficiency than existing approaches (SLEUTH and CSTPM) under comparable parameter settings.
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.