N. Micic, D. Neagu, I. Campean, und E. Habib Zadeh. (2017)Every industry has significant data output as a product of their working process, and with the recent advent of big data mining and integrated data warehousing it is the case for a robust methodology for assessing the quality for sustainable and consistent processing. In this paper a review is conducted on Data Quality (DQ) in multiple domains in order to propose connections between their methodologies. This critical review suggests that within the process of DQ assessment of heterogeneous data sets, not often are they treated as separate types of data in need of an alternate data quality assessment framework. We discuss the need for such a directed DQ framework and the opportunities that are foreseen in this research area and propose to address it through degrees of heterogeneity..
P. Missier, K. Belhajjame, und J. Cheney. Proceedings of the 16th International Conference on Extending Database Technology, Seite 773--776. New York, NY, USA, ACM, (2013)
Please include the acknowledgement in publications for those who are funded by our Cluster of Excellence:
Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 - 390740016