Authors:
Marcos Vieira
1
;
2
and
Sergio Carvalho
2
Affiliations:
1
Instituto Federal Goiano - IF Goiano, Brazil
;
2
Universidade Federal de Goiás - UFG, Brazil
Keyword(s):
Data Provenance, W3C PROV, Graphical Modeling, Metamodel, PROV-N, Modeling Tool, MDE.
Abstract:
The rise of the Internet of Things (IoT) and ubiquitous computing has led to a significant increase in data volumes, necessitating robust management. Data provenance is crucial for ensuring data reliability, integrity, and quality, tracking the origins, transformations, and movements of data. The W3C PROV standard, with syntaxes like PROV-N, provides textual and graphical representations for expressing and storing data provenance. However, despite its importance, there is a lack of user-friendly graphical tools for developers, particularly in IoT and ubiquitous computing. This paper addresses this gap by introducing an innovative graphical tool that enables the creation of user-friendly graphical data provenance models adhering to the W3C PROV standard. The tool offers an intuitive interface for developers, simplifying the process of obtaining PROV-N code from the generated provenance graph. We demonstrate the tool’s versatility across diverse domains, emphasizing its role in bridgin
g the gap in graphical provenance modeling. The paper outlines the Model-Driven Engineering (MDE) methodology used in the tool development, and introduces its underlying Ecore metamodel aligned with the PROV data model (PROV-DM). Evaluation results of the metamodel are presented, and potential applications of the tool are discussed, emphasizing its contribution to enhancing provenance-aware applications.
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