Name
Abstract | ||
|---|---|---|
Spatiotemporal event sequences represent the sequences of event types whose spatiotemporal instances frequently follow each other in spatiotemporal context. In this work, we present spatiotemporal event sequence mining from spatio-temporal event datasets that contains evolving region trajectories. We propose two algorithms for discovering spatio-temporal event sequences. We formally define a flexible spatiotemporal follow relationship, introduce various data models for capturing the sequence forming behavior. Lastly, we present an extended experimental evaluation that demonstrates the computational efficiency of our algorithms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/3004725.3004735 | MobiGIS |
Field | DocType | Citations |
Data mining,Data modeling,Computer science,Event sequence,Artificial intelligence,Deep learning,Spatiotemporal database,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Berkay Aydin | 1 | 40 | 10.75 |
| Rafal A. Angryk | 2 | 271 | 45.56 |