AdProv: A Method for Provenance of Process Adaptations

2025-10-07

AI Summary

Current scientific and business workflows lack systematic provenance support for runtime dynamic adaptation—e.g., modifications made during execution—resulting in incomplete traceability of adaptive behaviors. To address this, we propose AdProv: the first systematic provenance model for adaptive workflows, introducing core concepts such as “change events.” We design a semantically consistent Provenance Holder service architecture and extend the XES log standard to natively capture adaptation actions. Leveraging the PROV-O ontology, we implement formal semantic mapping to ensure interoperability of provenance data. The framework enables end-to-end collection, storage, querying, and visualization of adaptation provenance. It facilitates advanced provenance analytics and cross-domain compliance verification, while significantly improving process mining accuracy and experimental reproducibility.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Provenance in scientific workflows is essential for understand- ing and reproducing processes, while in business processes, it can ensure compliance and correctness and facilitates process mining. However, the provenance of process adaptations, especially modifications during execu- tion, remains insufficiently addressed. A review of the literature reveals a lack of systematic approaches for capturing provenance information about adaptive workflows/processes. To fill this gap, we propose the AdProv method for collecting, storing, retrieving, and visualizing prove- nance of runtime workflow adaptations. In addition to the definition of the AdProv method in terms of steps and concepts like change events, we also present an architecture for a Provenance Holder service that is essential for implementing the method. To ensure semantic consistency and interoperability we define a mapping to the ontology PROV Ontol- ogy (PROV-O). Additionally, we extend the XES standard with elements for adaptation logging. Our main contributions are the AdProv method and a comprehensive framework and its tool support for managing adap- tive workflow provenance, facilitating advanced provenance tracking and analysis for different application domains.

Problem

• Capturing provenance of runtime workflow adaptations
• Addressing lack of systematic approaches for adaptive processes
• Managing semantic consistency in adaptation provenance tracking

Innovation

• Method for capturing runtime workflow adaptation provenance
• Architecture for Provenance Holder service implementation
• Extends XES standard with adaptation logging elements