Managing Measurement and Occurrence Uncertainty in Complex Event Processing Systems

Nathalie Moreno,Loli Burgueno,Manuel F Bertoa,Antonio Vallecillo

doi:10.1109/access.2019.2923953

Abstract

Complex event processing (CEP) is a powerful technology for analyzing streams of real-time events, coming from different sources, and for extracting conclusions from them. In many situations, these events are not free from uncertainty, due to either unreliable data sources and networks, measurement uncertainty, or inability to determine whether an event has actually happened or not. This paper presents a proposal for incorporating and managing different kinds of uncertainty that may happen in both events and rules of the CEP systems. We provide a library that enables the representation and propagation of uncertain values, which can be efficiently integrated with the existing CEP languages and engines to deal with uncertainty, and we show how the treatment of uncertainty can be smoothly added to two of them: Esper and Apache Flink. Five applications coming from various domains serve to evaluate the proposal and to analyze its performance and accuracy. The results show that the overhead introduced by the treatment of uncertainty is not high and good precision and recall are achieved.

Highlights

Complex Event Processing (CEP) systems are being widely adopted as they provide effective means for processing and analyzing the steadily growing number of information sources that continuously produce and offer data in many applications of interest
One domain where CEP is relevant is the Internet of Things (IoT) [10], [11], where applications should process and react to events arriving from various kinds of sources including wireless sensor networks, RFID devices, GPS, etc
III describes our extension for dealing with uncertainty in CEP systems, and the implementation that we have developed for the Esper and Apache Flink CEP engines using our library for uncertainty

Summary

Introduction

Complex Event Processing (CEP) systems are being widely adopted as they provide effective means for processing and analyzing the steadily growing number of information sources that continuously produce and offer data in many applications of interest. Examples of such applications include monitoring systems for critical infrastructures [1], environmental monitoring [2]–[4], stock market analysis [5], network analysis and surveillance [6], maritime vessels trajectory monitoring [7], and social media data aggregation [8], [9]. We describe general CEP concepts and mechanisms, but to avoid ambiguity and for clarity and illustration purposes we will use the Esper EPL language

Objectives

Results

Conclusion