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    Querying log data with Metric Temporal Logic

    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 Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Dr Vladislav Ryzhikov
    Date Deposited: 15 Oct 2018 14:52
    Last Modified: 27 Jul 2019 16:58
    URI: http://eprints.bbk.ac.uk/id/eprint/24605

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    • Querying log data with Metric Temporal Logic. (deposited 15 Oct 2018 14:52) [Currently Displayed]

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