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Data-as-a-Service (DaaS) solutions make raw source data accessible in the form of processable information. Machine learning (ML) allows to produce meaningful information and knowledge based on raw data. Thus, quality is a major concern that applies to raw data as well as to information provided by ML-generated models. At the core of the solution is a conceptual framework that links input data quality and the machine learned data service quality, specifically inferring raw data problems as root causes from observed data service deficiency symptoms. This allows to deduce the hidden origins of quality problems observable by users of DaaS offerings. We analyse the quality framework through an extensive case study from an edge cloud and Internet-of-Thingsbased traffic application. We determine quality assessment mechanisms for symptom and cause analysis in different quality dimensions.

Continuous Data Quality Management for Machine Learning based Data-as-a-Service Architectures