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AdProv: A Method for Provenance of Process Adaptations

Ludwig Stage, Mirela Riveni, Raimundas Matulevičius, Dimka Karastoyanova

TL;DR

The paper tackles the shortage of systematic provenance for runtime workflow adaptations by introducing the AdProv method, a five-phase framework that captures and visualizes changes during execution. It defines an Adaptation XES extension for change events and maps provenance data to the PROV-O standard, enabling interoperable visualization and analysis. A core contribution is the Provenance Holder architecture, which supports collecting, storing, retrieving, and visualizing provenance across diverse workflow systems. By leveraging existing standards and process-mining concepts, AdProv facilitates reproducibility, compliance, and advanced analysis of adaptive processes in both business and scientific domains.

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.

AdProv: A Method for Provenance of Process Adaptations

TL;DR

The paper tackles the shortage of systematic provenance for runtime workflow adaptations by introducing the AdProv method, a five-phase framework that captures and visualizes changes during execution. It defines an Adaptation XES extension for change events and maps provenance data to the PROV-O standard, enabling interoperable visualization and analysis. A core contribution is the Provenance Holder architecture, which supports collecting, storing, retrieving, and visualizing provenance across diverse workflow systems. By leveraging existing standards and process-mining concepts, AdProv facilitates reproducibility, compliance, and advanced analysis of adaptive processes in both business and scientific domains.

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.

Paper Structure

This paper contains 14 sections, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. State of the Art
  3. The AdProv Method
  4. State of the Art Analysis
  5. Method Description
  6. Method Realization
  7. Method Realization
  8. Data Model for Change Events
  9. Provenance Holder Architecture
  10. Adaptation Visualization
  11. Feasibility Discussion
  12. Concluding Remarks
  13. Concluding Remarks
  14. Acknowledgments.

Figures (4)

  • Figure 1: AdProv method steps
  • Figure 2: Simple online shopping workflow with XES log examples
  • Figure 3: Provenance Holder Architecture components; adapter, external operations and internal methods (adopted from EDOC23)
  • Figure 4: Provenance graphs generated based on the PROV-O specification for the example in Figure\ref{['fig:log-events-to-provenance-info-xesext-int']}