Probabilistic simulation for probabilistic data-aware business processes

National audienceThere is a wide range of new applications that stress the need for business process models that are able to handle imprecise data. This paper studies the underlying modelling and analysis issues. It uses as formal model to describe process behaviours a labelled transitions system in which transitions are guarded by conditions defined over a probabilistic database and presents an approach for testing probabilistic simulation preorder in this context. A complexity analysis reveals that the problem is in 2-exptime, and is exptime-hard, w.r.t. expression complexity while it matches probabilistic query evaluation w.r.t. data-complexity

Test de simulation pour les processus métiers centrés données probabilistes

National audienceUn large éventail de nouvelles applications met l’accent sur la nécessité de disposer de modèles de processus métiers capables de manipuler des données imprécises ou incertaines. Cet article étudie les questions de modélisation et d’analyse sous-jacentes. Il utilise comme modèle formel pour décrire les comportements des processus métiers un système de transitions étiquetées dans lequel les transitions sont gardées par des conditions définies sur une base de données probabiliste. L’article présente une approche probabiliste pour tester la relation de simulation entre processus dans ce contexte. Une analyse de complexité révèle que le problème est dans 2-EXPTIME, et qu’il est EXPRIME-difficile en termes de complexité d’expression, alors que du point de vue de la complexité en termes des données, il n’engendre pas de surcoût supplémentaire par rapport au coût de l’évaluation de requêtes booléennes sur des bases de données probabilistes

PRODUS: un framework pour la vérification des modèles de processus probabilistiques

National audiencePRODUS is a probabilistic data-aware business process modeling and verification framework. PRODUS uses a formal model to describe process behaviors as a labelled transitions system in which transitions are guarded by conditions defined over a probabilistic database and implements algorithms for testing probabilistic simulation preorder and of model checking in this context. In this paper, we demonstrate the use of PRODUS on a case study in the area of managing insurance premium

Simulation probabiliste pour les processus métier orientés données probabilistes

International audienceThis paper studies modelling and analysis issues in the context of a probabilistic data-aware business process. It uses as formal model to describe process behaviours a labelled transitions system in which transitions are guarded by conditions defined over a probabilistic database and presents an approach for testing probabilistic simulation preorder in this context. A complexity analysis reveals that the problem is in 2-exptime, and is exptime-hard, w.r.t. expression complexity while it matches probabilistic query evaluation w.r.t. data-complexity

Better algorithms for analyzing and enacting declarative workflow languages using LTL

Declarative workflow languages are easy for humans to understand and use for specifications, but difficult for computers to check for consistency and use for enactment. Therefore, declarative languages need to be translated to something a computer can handle. One approach is to translate the declarative language to linear temporal logic (LTL), which can be translated to finite automata. While computers are very good at handling finite automata, the translation itself is often a road block as it may take time exponential in the size of the input. Here, we present algorithms for doing this translation much more efficiently (around a factor of 10,000 times faster and handling 10 times larger systems on a standard computer), making declarative specifications scale to realistic settings