An approach for iterative event pattern recommendation

Abstract

The need for systems that act on events is growing. Such systems require an infrastructure for detecting patterns over incoming events and tools for helping domain experts by creating or changing them. The main goal of Complex Event Processing is detecting patterns of events in near real-time in order to indicate a situation of interest. Nowadays most current Complex Event Processing systems are focused rather on run-time than on design time issues. They pay little attention to the efficient pattern generation. Moreover, in many Complex Event Processing systems, complex event patterns may change over time due to the dynamic nature of the domain. Such changes may complicate even further the specification task as the domain expert must update the patterns constantly. Therefore the experts seek for additional support for the definition of required patterns beyond expert opinion.In this paper we present an approach and its implementation that has been designed for a recommendation based pattern generation. We believe that a recommendation based pattern generation could increase the relevance and efficiency of newly generated patterns for the problem at hand by reusing knowledge coded in existing complex event patterns.