Probabilistic Complex Event Recognition: A Survey

TL;DR

This survey reviews probabilistic techniques for complex event recognition, discussing their methods, limitations, and future research directions in handling uncertainty in data streams and pattern detection.

Contribution

It provides a comprehensive overview of existing probabilistic approaches, comparing their models and identifying key limitations and promising future research directions.

Findings

Automata, probabilistic graphical models, and first-order logic are common techniques.

Limitations include language expressiveness, probabilistic modeling, and performance issues.

Future work should address these limitations to improve probabilistic complex event recognition.

Abstract

Complex Event Recognition applications exhibit various types of uncertainty, ranging from incomplete and erroneous data streams to imperfect complex event patterns. We review Complex Event Recognition techniques that handle, to some extent, uncertainty. We examine techniques based on automata, probabilistic graphical models and first-order logic, which are the most common ones, and approaches based on Petri Nets and Grammars, which are less frequently used. A number of limitations are identified with respect to the employed languages, their probabilistic models and their performance, as compared to the purely deterministic cases. Based on those limitations, we highlight promising directions for future work.