Grand challenge: Venilia, on-line learning and prediction of vessel destination

Moti Bachar, Gal Elimelech, Itai Gat, Gil Sobol, Nicolo Rivetti, Avigdor Gal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationDEBS 2018 - Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems
Pages209-212
Number of pages4
ISBN (Electronic)9781450357821
DOIs
StatePublished - 25 Jun 2018
Event12th ACM International Conference on Distributed and Event-Based Systems, DEBS 2018 - Hamilton, New Zealand
Duration: 25 Jun 201826 Jun 2018

Publication series

NameDEBS 2018 - Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems

Conference

Conference12th ACM International Conference on Distributed and Event-Based Systems, DEBS 2018
Country/TerritoryNew Zealand
CityHamilton
Period25/06/1826/06/18

Keywords

  • AIS
  • Complex Event Processing
  • Probabilistic Prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Networks and Communications
  • Software
  • Hardware and Architecture

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