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Quality evaluation for big data

A scalable assessment approach and first evaluation results
: Kläs, Michael; Putz, Wolfgang; Lutz, Tobias


Heidrich, Jens (Ed.); Vogelezang, Frank (Ed.) ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IWSM-Mensura 2016, 26th International Workshop on Software Measurement (IWSM) and the 11th International Conference on Software Process and Product Measurement (Mensura). Proceedings : 5-7 October 2016, Berlin, Germany
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2016
ISBN: 978-1-5090-4147-3
ISBN: 978-1-5090-4148-0
International Workshop on Software Measurement (IWSM) <26, 2016, Berlin>
International Conference on Software Process and Product Measurement (Mensura) <11, 2016, Berlin>
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 multi-node 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.