Name
Abstract | ||
|---|---|---|
With the advancements in spatiotemporal co-occurrence pattern and event sequence mining algorithms, spatiotemporal knowledge discovery from solar event datasets has been prominent in solar data mining. This work presents an efficient and extensible data access mechanism specifically designed for spatiotemporal relationships among the solar event instances. Previous indexing strategies primarily focus on indexing the trajectories of solar event instances. We propose a graph-based indexing structure for spatiotemporal relationships appearing among the event instances. In our graph structure, the vertices correspond to trajectory-based solar event instances, and the edges represent a particular spatiotemporal relationship. Furthermore, it forms the groundwork for semantic representations of solar event instances. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | Spatiotemporal Knowledge Discovery, Sequence Patterns, Event Sequence Mining |
Field | DocType | Citations |
Data mining,Graph,Computer science,Search engine indexing,Event sequence,Knowledge extraction,Big data,Data access,Semantics,Trajectory | Conference | 0 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Berkay Aydin | 1 | 40 | 10.75 |
| Ahmet Kucuk | 2 | 3 | 2.42 |
| Rafal Angryk | 3 | 8 | 2.57 |