MtDetector: A High-performance Marine Traffic Detector at Stream Scale

Chun-Xun Lin, Tsung-Wei Huang, Guannan Guo, Martin D. F. Wong

Research output: Chapter in book/report/conference proceedingChapterpeer-review

9 Citations (Scopus)

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 languageEnglish
Title of host publicationDEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems
PublisherAssociation for Computing Machinery (ACM)
Pages205–208
Number of pages4
ISBN (Electronic)9781450357821
DOIs
Publication statusPublished - Jun 2018
EventDEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems (2018) - Hamilton, New Zealand
Duration: 25 Jun 201829 Jun 2018
https://dl.acm.org/doi/proceedings/10.1145/3210284 (Link to conference proceedings)

Publication series

NameProceedings of ACM International Conference on Distributed and Event-based Systems

Conference

ConferenceDEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems (2018)
Country/TerritoryNew Zealand
CityHamilton
Period25/06/1829/06/18
Internet address

User-Defined Keywords

  • Distributed System
  • Marine Traffic
  • Machine Learning
  • Stream Processing

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