Moti Bachar, Gal Elimelech, Itai Gat, Gil Sobol, Nicolo Rivetti, A. Gal
{"title":"Venilia,船舶目的地在线学习与预测","authors":"Moti Bachar, Gal Elimelech, Itai Gat, Gil Sobol, Nicolo Rivetti, A. Gal","doi":"10.1145/3210284.3220505","DOIUrl":null,"url":null,"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.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Venilia, On-line Learning and Prediction of Vessel Destination\",\"authors\":\"Moti Bachar, Gal Elimelech, Itai Gat, Gil Sobol, Nicolo Rivetti, A. Gal\",\"doi\":\"10.1145/3210284.3220505\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":412438,\"journal\":{\"name\":\"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3210284.3220505\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3210284.3220505","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Venilia, On-line Learning and Prediction of Vessel Destination
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.