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

arXiv: https://arxiv.org/abs/2607.05995 · HTML: https://mlsanity.com/paper/2607.05995/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 4 of 4)

- [A Countrywide Traffic Accident Dataset](https://mlsanity.com/paper/1906.05409/) (2019)
- [Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data](https://mlsanity.com/paper/1902.06792/) (2019)
- [Spatiotemporal Data Mining: A Survey on Challenges and Open Problems](https://mlsanity.com/paper/2103.17128/) (2021)
- [Discovery of Crime Event Sequences with Constricted Spatio-Temporal Sequential Patterns](https://mlsanity.com/paper/2112.01863/) (2021)

## Abstract (arXiv)

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.