DatalogMTL: Datalog with Metric Temporal Logic
                         Operators (Abstract)
                         Przemysław Andrzej Wałęga
                         Queen Mary University of London, University of Oxford, UK


                                      Abstract
                                      DatalogMTL is a powerful extension of Datalog, designed to handle complex temporal reasoning. In this
                                      framework, a temporal dataset consists of facts that hold over intervals along a rational timeline. A DatalogMTL
                                      program enables recursive reasoning over these facts, by incorporating metric temporal operators—a metric
                                      extension of linear temporal logic (LTL) operators. This results in an expressive language that enhances Datalog’s
                                      capabilities and opens the door to a range of potential applications.
                                      While the temporal aspect introduces significant computational challenges, several reasoning approaches for
                                      DatalogMTL have been proposed, with some already seeing implementation. DatalogMTL has also been further
                                      extended with features such as non-monotonic negation, existential rules, and temporal aggregation, introducing
                                      even more complex behaviour.
                                      During the talk, I will introduce the line of research on DatalogMTL. I will discuss properties of DatalogMTL,
                                      focussing on the reasoning algorithms and analysis of computational complexity.




                          Datalog 2.0 2024: 5th International Workshop on the Resurgence of Datalog in Academia and Industry, October 11, 2024, Dallas,
                          Texas, USA
                          $ przemyslaw.walega@cs.ox.ac.uk (P. A. Wałęga)
                                   © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).


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