# Complex Event Recognition with Symbolic Register Transducers: Extended Technical Report

arXiv: https://arxiv.org/abs/2407.02884 · HTML: https://mlsanity.com/paper/2407.02884/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 3 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Wayeb: a Tool for Complex Event Forecasting](https://mlsanity.com/paper/1901.01826/) (2018)
- [Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report](https://mlsanity.com/paper/2109.00287/) (2021)
- [CORE: a Complex Event Recognition Engine](https://mlsanity.com/paper/2111.04635/) (2021)
- [From Complex Event Processing to Simple Event Processing](https://mlsanity.com/paper/1702.08051/) (2017)

## Built on by (most cited first, 1 of 1)

- [Efficient Temporal Datalog Materialisation for Composite Event Recognition](https://mlsanity.com/paper/2605.02488/) (2026)

## Abstract (arXiv)

We present a system for Complex Event Recognition (CER) based on automata. While multiple such systems have been described in the literature, they typically suffer from a lack of clear and denotational semantics, a limitation which often leads to confusion with respect to their expressive power. In order to address this issue, our system is based on an automaton model which is a combination of symbolic and register automata. We extend previous work on these types of automata, in order to construct a formalism with clear semantics and a corresponding automaton model whose properties can be formally investigated. We call such automata Symbolic Register Transducers (SRT). We show that SRT are closed under various operators, but 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 SRT can be used in CER 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. For SRT to work in pattern detection, we allow them to mark events from the input stream as belonging to a complex event or not, hence the name "transducers". We also present an implementation of SRT which can perform CER. We compare our SRT-based CER engine against other state-of-the-art CER systems and show that it is both more expressive and more efficient.