@inproceedings{ba886b4b0b4a4ec5a3aa47c9be118b9e,
title = "Grand challenge: Venilia, on-line learning and prediction of vessel destination",
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.",
keywords = "AIS, Complex Event Processing, Probabilistic Prediction",
author = "Moti Bachar and Gal Elimelech and Itai Gat and Gil Sobol and Nicolo Rivetti and Avigdor Gal",
note = "Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s).; 12th ACM International Conference on Distributed and Event-Based Systems, DEBS 2018 ; Conference date: 25-06-2018 Through 26-06-2018",
year = "2018",
month = jun,
day = "25",
doi = "10.1145/3210284.3220505",
language = "الإنجليزيّة",
series = "DEBS 2018 - Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems",
pages = "209--212",
booktitle = "DEBS 2018 - Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems",
}