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Venilia, On-line Learning and Prediction of Vessel Destination

Published: 25 June 2018 Publication History

Abstract

The ACM DEBS 2018 Grand Challenge focuses on (soft) real-time prediction of both the destination port and the time of arrival of vessels, monitored through the Automated Identification System (AIS). Venilia prediction mechanism is based on a variety of machine learning techniques, including Markov predictive models. To improve the accuracy of a model, trained off-line on historical data, Venilia supports also on-line continuous training using an incoming event stream. The software architecture enables a low latency, highly parallelized, and load balanced prediction pipeline. Aiming at a portable and reusable solution, Venilia is implemented on top of the Akka Actor framework. Finally, Venilia is also equipped with a visualization tool for data exploration.

References

[1]
J. L. Carter and M. N. Wegman. 1979. Universal Classes of Hash Functions. Journal of Computer and System Sciences 18 (1979), 143--154.
[2]
C. Chen, C. Lu, Q. Huang, Q. Yang, D. Gunopulos, and L. Guibas. 2016. City-Scale Map Creation and Updating Using GPS Collections. In Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining (KDD).
[3]
A. Gal, A. Mandelbaum, F. Schnitzler, A. Senderovich, and M. Weidlich. 2017. Traveling time prediction in scheduled transportation with journey segments. Information Systems 64 (2017), 266 -- 280.
[4]
V. Gulisano, Z. Jerzak, P. Smirnov, M. Strohbach, and H. Ziekow. 2018. The DEBS 2018 grand challenge. In Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems (DEBS).
[5]
Lightbend. Akka. https://akka.io/.
[6]
LMAX-Exchange. LMAX Disruptor. https://lmax-exchange.github.io/disruptor/.
[7]
M. A. U. Nasir, G. De Francisci Morales, D. G. Soriano, N. Kourtellis, and M. Serafini. 2015. The Power of Both Choices: Practical Load Balancing for Distributed Stream Processing Engines. In Proceedings of the 31st IEEE International Conference on Data Engineering (ICDE).
[8]
Pivotal Software, Inc. RabbitMQ. https://www.rabbitmq.com/.
[9]
The Apache Software Foundation. Apache Flink. https://flink.apache.org/.

Cited By

View all
  • (2025)A Review of Vessel Time of Arrival Prediction on Waterway Networks: Current Trends, Open Issues, and Future DirectionsComputers10.3390/computers1402004114:2(41)Online publication date: 28-Jan-2025
  • (2020)Classification of vessel activity in streaming dataProceedings of the 14th ACM International Conference on Distributed and Event-based Systems10.1145/3401025.3401763(153-164)Online publication date: 13-Jul-2020

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cover image ACM Conferences
DEBS '18: Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems
June 2018
289 pages
ISBN:9781450357821
DOI:10.1145/3210284
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2018

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Author Tags

  1. AIS
  2. Complex Event Processing
  3. Probabilistic Prediction

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  • Short-paper
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  • Refereed limited

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DEBS '18

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DEBS '18 Paper Acceptance Rate 12 of 31 submissions, 39%;
Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

View all
  • (2025)A Review of Vessel Time of Arrival Prediction on Waterway Networks: Current Trends, Open Issues, and Future DirectionsComputers10.3390/computers1402004114:2(41)Online publication date: 28-Jan-2025
  • (2020)Classification of vessel activity in streaming dataProceedings of the 14th ACM International Conference on Distributed and Event-based Systems10.1145/3401025.3401763(153-164)Online publication date: 13-Jul-2020

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