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Enhancing Declarative Temporal Model Mining in Relational Databases: A Preliminary Study

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



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


Propositionalisation tampers the running time of state-of-the-art algorithms in declarative temporal model mining, as they exhaustively generate the clauses instantiated with the results of frequent itemset mining algorithms. Existing algorithms also exploit non-indexed data representations, thus negatively affecting the overall running time. This paper proposes a novel temporal model mining algorithm, Bolt, twinning confidence and support metrics as heuristics for candidate pruning with data structures enabling fast temporal data scanning. Bolt outperforms both state-of-the-art and renditions of existing mining algorithms using KnoBAB as a library

Publication metadata

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

Editor(s): Chbeir R; Ivanovic M; Manolopoulos Y; Revesz PZ

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IDEAS23: International Database Engineered Applications Symposium Conference

Year of Conference: 2023

Pages: 34-42

Online publication date: 26/05/2023

Acceptance date: 21/03/2023

Date deposited: 27/05/2023

Publisher: ACM


DOI: 10.1145/3589462.3589491

Library holdings: Search Newcastle University Library for this item

ISBN: 9798400707445