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Quality Evaluation for Big Data

A Scalable Assessment Approach and First Evaluation Results
: Kläs, Michael; Putz, Wolfgang; Lutz, Tobias

Kaiserslautern, 2016, 10 S.
IESE-Report, 028.16/E
Reportnr.: 028.16/E
Fraunhofer IESE ()
QuaMoCo; Big Data; quality assessment; software quality measurement

High-quality data is a prerequisite for most types of analysis provided by software systems. However, since data quality does not come for free, it has to be assessed and managed continuously. The increasing quantity, diversity, and velocity that characterize big data today make these tasks even more challenging. We identified challenges that are specific for big data quality assessments with particular emphasis on their usage in smart ecosystems and make a proposal for a scalable cross organizational approach that addresses these challenges. We developed an initial prototype to investigate scalability in a multinode test environment using big data technologies. Based on the observed horizontal scalability behavior, there is an indication that the proposed approach also allows dealing with increasing volumes of heterogeneous data.