Querying Log Data with Metric Temporal Logic
Abstract
We propose a novel framework for ontology-based access to temporal log data using a datalog extension datalogMTL of the Horn fragment of the metric temporal logic MTL. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case full MTL is known to be undecidable. We also prove that nonrecursive datalogMTL is PSPACE-complete for combined complexity and in AC0 for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate access to temporal log data. Our experiments with Siemens turbine data and MesoWest weather data show that datalogMTL ontology-mediated queries are efficient and scale on large datasets.
Cite
Text
Brandt et al. "Querying Log Data with Metric Temporal Logic." Journal of Artificial Intelligence Research, 2018. doi:10.1613/JAIR.1.11229Markdown
[Brandt et al. "Querying Log Data with Metric Temporal Logic." Journal of Artificial Intelligence Research, 2018.](https://mlanthology.org/jair/2018/brandt2018jair-querying/) doi:10.1613/JAIR.1.11229BibTeX
@article{brandt2018jair-querying,
title = {{Querying Log Data with Metric Temporal Logic}},
author = {Brandt, Sebastian and Kalayci, Elem Güzel and Ryzhikov, Vladislav and Xiao, Guohui and Zakharyaschev, Michael},
journal = {Journal of Artificial Intelligence Research},
year = {2018},
pages = {829-877},
doi = {10.1613/JAIR.1.11229},
volume = {62},
url = {https://mlanthology.org/jair/2018/brandt2018jair-querying/}
}