Quickening Data-Aware Conformance Checking through Temporal Algebras
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Published:2023-03-08
Issue:3
Volume:14
Page:173
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ISSN:2078-2489
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Container-title:Information
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language:en
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Short-container-title:Information
Author:
Bergami Giacomo1ORCID, Appleby Samuel1ORCID, Morgan Graham1ORCID
Affiliation:
1. School of Computing, Faculty of Science, Agriculture and Engineering, Newcastle University, Newcastle Upon Tyne NE4 5TG, UK
Abstract
A temporal model describes processes as a sequence of observable events characterised by distinguishable actions in time. Conformance checking allows these models to determine whether any sequence of temporally ordered and fully-observable events complies with their prescriptions. The latter aspect leads to Explainable and Trustworthy AI, as we can immediately assess the flaws in the recorded behaviours while suggesting any possible way to amend the wrongdoings. Recent findings on conformance checking and temporal learning lead to an interest in temporal models beyond the usual business process management community, thus including other domain areas such as Cyber Security, Industry 4.0, and e-Health. As current technologies for accessing this are purely formal and not ready for the real world returning large data volumes, the need to improve existing conformance checking and temporal model mining algorithms to make Explainable and Trustworthy AI more efficient and competitive is increasingly pressing. To effectively meet such demands, this paper offers KnoBAB, a novel business process management system for efficient Conformance Checking computations performed on top of a customised relational model. This architecture was implemented from scratch after following common practices in the design of relational database management systems. After defining our proposed temporal algebra for temporal queries (xtLTLf), we show that this can express existing temporal languages over finite and non-empty traces such as LTLf. This paper also proposes a parallelisation strategy for such queries, thus reducing conformance checking into an embarrassingly parallel problem leading to super-linear speed up. This paper also presents how a single xtLTLf operator (or even entire sub-expressions) might be efficiently implemented via different algorithms, thus paving the way to future algorithmic improvements. Finally, our benchmarks highlight that our proposed implementation of xtLTLf (KnoBAB) outperforms state-of-the-art conformance checking software running on LTLf logic.
Subject
Information Systems
Reference59 articles.
1. Mining Association Rules between Sets of Items in Large Databases;Agrawal;SIGMOD Rec.,1993 2. Bergami, G., Maggi, F.M., Montali, M., and Peñaloza, R. (November, January 31). Probabilistic Trace Alignment. Proceedings of the 2021 3rd International Conference on Process Mining (ICPM), Eindhoven, The Netherlands. 3. Schön, O., van Huijgevoort, B., Haesaert, S., and Soudjani, S. (2022, January 6–9). Correct-by-Design Control of Parametric Stochastic Systems. Proceedings of the 2022 IEEE 61st Conference on Decision and Control, Cancun, Mexico. 4. Appleby, S., Bergami, G., and Morgan, G. (2022, January 22–24). Running Temporal Logical Queries on the Relational Model. Proceedings of the International Database Engineered Applications Symposium (IDEAS’22), Budapest, Hungary. 5. Schönig, S., Rogge-Solti, A., Cabanillas, C., Jablonski, S., and Mendling, J. (2016). Advanced Information Systems Engineering, Proceedings of the 28th International Conference, CAiSE 2016, Ljubljana, Slovenia, 13–17 June 2016, Springer.
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