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Applying a Mapping Quality Framework in Cloud Native Monitoring

1 The linked data generation process is a complex process which involves multiple stakeholders in the definition of mapping artefacts which transform source data into linked data representation. The creation of these mapping artefacts is error prone, and the quality of the artefacts should be assessed prior to the generation of the linked data. Producing high quality mappings will result in high quality linked data and provide higher confidence to linked data consumers. Furthermore, the source data of these mappings should be regularly monitored to detect data changes which could impact the quality of the resulting linked data dataset. A process designed to offer fresh and high-quality data will benefit both data consumers and producers. Applying the process to a real-world use case demonstrates the feasibility and effectiveness of the process. In this paper we describe a process designed to improve quality within the linked data generation process. Furthermore, we describe the application of the process to a real-world cloud monitoring use case for the purpose of producing high quality mappings. Details of the core assessment and refinement aspects of the MQV framework is being presented in a paper in the research and innovation track of the SEMANTiCS 2022 conference. The framework has recently been extended to include a change detection component. The component is designed to ensure the freshness of linked data generated through the mapping artefacts by detecting and analyzing source data changes. The framework enables the impact of these detected changes to be assessed, and necessary action can be taken to ensure the freshness of the associated linked data for consumers.