Name
Abstract | ||
|---|---|---|
The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems. |
Year | Venue | Field |
|---|---|---|
2017 | EDBT | Data integration,Data science,Data mining,Visualization,Computer science,Streaming data |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christophe Claramunt | 1 | 846 | 107.03 |
| Cyril Ray | 2 | 167 | 20.53 |
| Elena Camossi | 3 | 2 | 1.39 |
| Anne-laure Jousselme | 4 | 500 | 30.08 |
| Melita Hadzagic | 5 | 4 | 0.73 |
| Gennady Andrienko | 6 | 3106 | 208.19 |
| Natalia Andrienko | 7 | 2922 | 192.14 |
| Y. Theodoridis | 8 | 168 | 20.62 |
| George A. Vouros | 9 | 819 | 87.44 |
| Loïc Salmon | 10 | 0 | 0.68 |