Symbolic Register Automata for Complex Event Recognition and Forecasting

TL;DR

This paper introduces Symbolic Register Automata (SRA), a new automaton model that combines symbolic and register automata features, enabling complex event recognition and forecasting with enhanced expressive power and probabilistic analysis.

Contribution

The paper defines SRA, studies their closure properties, and demonstrates their application in complex event recognition and forecasting, extending existing automata models.

Findings

SRA are closed under union, intersection, concatenation, Kleene closure, and window-based complement and determinization.

SRA can detect complex event patterns in streams with declarative semantics.

SRA enable probabilistic forecasting of event patterns using prediction suffix trees.

Abstract

We propose an automaton model which is a combination of symbolic and register automata, i.e., we enrich symbolic automata with memory. We call such automata Symbolic Register Automata (SRA). SRA extend the expressive power of symbolic automata, by allowing Boolean formulas to be applied not only to the last element read from the input string, but to multiple elements, stored in their registers. SRA also extend register automata, by allowing arbitrary Boolean formulas, besides equality predicates. We study the closure properties of SRA under union, intersection, concatenation, Kleene closure, complement and determinization and show that SRA, contrary to symbolic automata, are not in general closed under complement and they are not determinizable. However, they are closed under these operations when a window operator, quintessential in Complex Event Recognition, is used. We show how SRA can be used in Complex Event Recognition in order to detect patterns upon streams of events, using our framework that provides declarative and compositional semantics, and that allows for a systematic treatment of such automata. We also show how the behavior of SRA, as they consume streams of events, can be given a probabilistic description with the help of prediction suffix trees. This allows us to go one step beyond Complex Event Recognition to Complex Event Forecasting, where, besides detecting complex patterns, we can also efficiently forecast their occurrence.