Abstract
In this paper, we present MtDetector, a high performance marine traffic detector that can predict the destination and the arrival time of travelling vessels. MtDetector accepts streaming data reported by the moving vessels and generates continuous predictions of the arrival port and arrival time for those vessels. To predict the destination for a ship, MtDetector builds a neural network for every port and infers the arrival port for vessels based on their departure port. For the arrival time prediction, we derive informative features from training data and apply Deep Neural Network (DNN) to estimate the traveling time. MtDetector is built on top of DtCraft [1,2], a high-performance distributed execution engine for stream programming. By utilizing the task-based parallelism in DtCraft, MtDetector can process multiple predictions concurrently to achieve high throughput and low latency.
| Original language | English |
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| Title of host publication | DEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 205–208 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781450357821 |
| DOIs | |
| Publication status | Published - Jun 2018 |
| Event | DEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems (2018) - Hamilton, New Zealand Duration: 25 Jun 2018 → 29 Jun 2018 https://dl.acm.org/doi/proceedings/10.1145/3210284 (Link to conference proceedings) |
Publication series
| Name | Proceedings of ACM International Conference on Distributed and Event-based Systems |
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Conference
| Conference | DEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems (2018) |
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| Country/Territory | New Zealand |
| City | Hamilton |
| Period | 25/06/18 → 29/06/18 |
| Internet address |
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User-Defined Keywords
- Distributed System
- Marine Traffic
- Machine Learning
- Stream Processing
