{"ID":6023454,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T08:15:11.905439937Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05995","arxiv_id":"2607.05995","title":"Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data","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.","short_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 th...","url_abs":"https://arxiv.org/abs/2607.05995","url_pdf":"https://arxiv.org/pdf/2607.05995v1","authors":"[\"Piotr S. Maciąg\"]","published":"2026-07-07T08:30:06Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.LG\"]","methods":"[]","has_code":false}