Brandt, S. and Güzel Kalaycı, E. and Ryzhikov, Vladislav and Xiao, G. and Zakharyaschev, Michael (2018) Querying log data with Metric Temporal Logic. Artificial Intelligence Research 62 , pp. 829-877. ISSN 1927-6974.
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Abstract
We propose a novel framework for ontology-based access to temporal log data using a datalog extension datalogMTL of a 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 of up to 8.3GB.
Metadata
| Item Type: | Article |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Vladislav Ryzhikov |
| Date Deposited: | 15 Oct 2018 14:52 |
| Last Modified: | 09 Aug 2023 12:45 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/24605 |
Available Versions of this Item
- Querying log data with Metric Temporal Logic. (deposited 15 Oct 2018 14:52) [Currently Displayed]
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