Wayeb: a Tool for Complex Event Forecasting
Artificial Intelligence
2019-01-08 v1 Formal Languages and Automata Theory
Abstract
Complex Event Processing (CEP) systems have appeared in abundance during the last two decades. Their purpose is to detect in real-time interesting patterns upon a stream of events and to inform an analyst for the occurrence of such patterns in a timely manner. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CEP engine. We present Wayeb, a tool that attempts to address the issue of Complex Event Forecasting. Wayeb employs symbolic automata as a computational model for pattern detection and Markov chains for deriving a probabilistic description of a symbolic automaton.
Cite
@article{arxiv.1901.01826,
title = {Wayeb: a Tool for Complex Event Forecasting},
author = {Elias Alevizos and Alexander Artikis and Georgios Paliouras},
journal= {arXiv preprint arXiv:1901.01826},
year = {2019}
}