A DBMS Framework for Diagnosability Analysis of Discrete Event Systems

During the last decades, various techniques have been developed to deal with diagnosis issues on Discrete Event Systems (DES). These techniques, even if they have reached a good maturity degree, need the elaboration of intermediate ad-hoc models from the original model, in order to proceed to diagnosability investigation. Instead of developing ad-hoc products between models, we have suggested a new formulation of DES diagnosability issue using a unique logical framework, μ-calculus. Therefore, diagnosability analysis is performed through the computing of successive logical relations. In this paper, a brief overview of this formulation is given, then we develop an original implementation of diagnosability investigation on the basis of the formulation. Our implementation consists of a DBMS-architecture (Database Management System) where system behavior is encoded as a set of relational tables and where diagnosability investigation is performed through an ordered sequence of queries on these tables. With this successful prototype implementation, we are confident to pave the way for applying the method on real-size systems while taking advantage of the performances of new DBMS inference engines, the decentralization facilities on DBMS and of new technologies for external storing hardware. To our knowledge, this is the first work that employs a DBMS architecture to perform diagnosability analysis on DES.

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Source DSN 2012, 42nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks
Author Ghazel, Mohamed, Peres, Florent, Belhaj Alaya, Atef, Jemai, Abderrazak
Maintainer CCSD
Last Updated May 10, 2026, 00:30 (UTC)
Created May 10, 2026, 00:30 (UTC)
Identifier hal-00854280
Language en
contributor Évaluation des Systèmes de Transports Automatisés et de leur Sécurité (IFSTTAR/ESTAS) ; Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Lille Nord de France
creator Ghazel, Mohamed
date 2012-06-25T00:00:00
harvest_object_id 3c9d0733-4004-4cbb-b830-2ce994f11972
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2025-08-27T00:00:00
set_spec type:COMM