Wayeb: a Tool for Complex Event Forecasting
This addresses a gap in real-time event processing for analysts, but appears incremental as it builds on existing CEP methods.
The paper tackles the problem of forecasting when complex event patterns might occur before detection by CEP systems, presenting Wayeb as a tool that uses symbolic automata and Markov chains to achieve this.
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.