Incremental Event Calculus for Run-Time Reasoning (Extended Abstract)
Abstract
We present a system for online, incremental composite event recognition. In streaming environments, the usual case is for data to arrive with a (variable) delay from, and to be revised by, the underlying sources. We propose RTEC_inc, an incremental version of RTEC, a composite event recognition engine with formal, declarative semantics, that has been shown to scale to several real-world data streams. RTEC deals with delayed arrival and revision of events by computing all queries from scratch. This is often inefficient since it results in redundant computations. Instead, RTEC_inc deals with delays and revisions in a more efficient way, by updating only the affected queries. We compare RTEC_inc and RTEC experimentally using real-world and synthetic datasets. The results are compatible with our complexity analysis and show that RTEC_inc outperforms RTEC in many practical cases.
Cite
Text
Tsilionis et al. "Incremental Event Calculus for Run-Time Reasoning (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/793Markdown
[Tsilionis et al. "Incremental Event Calculus for Run-Time Reasoning (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/tsilionis2023ijcai-incremental/) doi:10.24963/IJCAI.2023/793BibTeX
@inproceedings{tsilionis2023ijcai-incremental,
title = {{Incremental Event Calculus for Run-Time Reasoning (Extended Abstract)}},
author = {Tsilionis, Efthimis and Artikis, Alexander and Paliouras, Georgios},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {6974-6978},
doi = {10.24963/IJCAI.2023/793},
url = {https://mlanthology.org/ijcai/2023/tsilionis2023ijcai-incremental/}
}