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An Ordered Chronicle Discovery Algorithm

Alexandre Sahuguède 1 Euriell Le Corronc 1 Marie-Véronique Le Lann 1
1 LAAS-DISCO - Équipe DIagnostic, Supervision et COnduite
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : Chronicles are temporal patterns well suited to capture dynamic process thanks to an event abstraction of the information of interest. Designing chronicles from a journal log is not a trivial task considering the huge amount of data generated by highly-advanced systems. Chronicle discovery is a mean to help expert design chronicles that are representative of a system behavior from direct observations. In this paper, a clustering approach to the chronicle discovery problem is considered. To improve the discovered chronicle quality, an order in the design of interesting pattern is introduced. This allows a better robustness to small perturbations in the input journal log. The efficiency of the ordered chronicle discovery algorithm is evaluated on a real dataset.
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Contributor : Alexandre Sahuguède <>
Submitted on : Thursday, December 6, 2018 - 4:37:44 PM
Last modification on : Thursday, June 10, 2021 - 3:01:35 AM
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  • HAL Id : hal-01947338, version 1


Alexandre Sahuguède, Euriell Le Corronc, Marie-Véronique Le Lann. An Ordered Chronicle Discovery Algorithm. 3nd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD'18, Sep 2018, Dublin, Ireland. ⟨hal-01947338⟩



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