Legal-URN Framework for Legal Compliance of Business Processes
Notice bibliographique
Résumé
In recent years, the number of regulations an organization needs to comply with has been increasing, and organizations have to ensure that their business processes are aligned with these regulations. However, because of the complexity and intended vagueness of regulations in general, it is not possible to treat them the same way as other types of requirements. On the other hand, the cost of being non-compliant can also be fairly high; non-compliance can cause crucial harm to the organization with financial penalties or loss of reputation. Therefore, it is very important for organizations to take a systematic approach to ensuring that their compliance with related laws, regulations and standards is established and maintained. To achieve this goal, this thesis proposes a model-based compliance analysis framework for business processes called Legal-URN. This framework is composed of four layers of abstraction linked to each other. The framework exploits the User Requirements Notation (URN) as the modeling language to describe and combine legal and organizational models. In order to model legal documents, legal statements are first classified into four classes of Hohfeldian rights, and then Hohfeldian models of the regulations and their statements are created. These models are further refined into legal goal and business process models via a domain-specific version of URN called Legal URN profile. To check the well-formedness of the models and to identify instances of non-compliance, 23 Object Constraint Language (OCL) rules are provided. In this thesis, the quantitative and qualitative analysis algorithms of URN's Goal-oriented Requirement Language are extended to help analyze quantitatively and qualitatively the degree of compliance of an organization to the legal models. Furthermore, with the help of a prioritization algorithm, the framework enables one to decide, while taking the organization goals into consideration, which non-compliant instances to address first in order to provide a suitable evolution path for business processes. In addition, to assess compliance with more than one regulation, a pair-wise comparison algorithm enables organizations to identify the similarities and conflicts among regulations and incorporate them in the models. The jUCMNav tool, an Eclipse plug-in for URN modeling and analysis, was extended to support the framework and its algorithms and rules. The thesis contributions are evaluated through a gap analysis based on a systematic literature review, a comparison with closely related work, and two case studies in the healthcare domain: one with a single regulation and realistic business processes, and a second with three additional regulations. We also identify the benefits and limitations of the framework, as well as potential extensions for future work. The Legal-URN framework provides a tool-supported, rigorous approach to compliance analysis of organizations against relevant regulations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,005 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».