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Specification Mining over Temporal Data

Lookup NU author(s): Dr Giacomo BergamiORCiD, Sam Appleby, Professor Graham MorganORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

Current specification mining algorithms for temporal data rely on exhaustive search approaches, which become detrimental in real data settings where a plethora of distinct temporal behaviours are recorded over prolonged observations. This paper proposes a novel algorithm, Bolt2, based on a refined heuristic search of our previous algorithm, Bolt. Our experiments show that the proposed approach not only surpasses exhaustive search methods in terms of running time but also guarantees a minimal description that captures the overall temporal behaviour. This is achieved through a hypothesis lattice search that exploits support metrics. Our novel specification mining algorithm also outperforms the results achieved in our previous contribution.

Publication metadata

Author(s): Bergami G, Appleby S, Morgan G

Publication type: Article

Publication status: Published

Journal: Computers

Year: 2023

Volume: 12

Issue: 9

Online publication date: 14/09/2023

Acceptance date: 11/09/2023

Date deposited: 14/09/2023

ISSN (electronic): 2073-431X

Publisher: MDPI

URL: https://doi.org/10.3390/computers12090185

DOI: 10.3390/computers12090185

Data Access Statement: The dataset associated with the presented experiments is available online at https://osf.io/nsqcd/ and https://osf.io/69q8h/ (accessed on 10 September 2023).

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