Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data

• URL: http://arxiv.org/abs/2607.05995v1
• Date: Tue, 07 Jul 2026 08:30:06 GMT
• Title: Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data
• Abstract summary: We propose a novel approach to mine patterns intemporal-temporal event data based on discovering closed embedded sub-Acyclic Graphs (DAGs)<n>In our method, event instances are represented as nodes labelled by event types, while recurring edges capture-temporal following relationships.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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