Towards a Data Quality Framework for Heterogeneous Data
N. Micic, D. Neagu, I. Campean, and 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..
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.
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%0 Conference Paper
%1 micic2017towards
%A Micic, Natasha
%A Neagu, Daniel
%A Campean, I. Felician
%A Habib Zadeh, Esmaeil
%D 2017
%K forschungsdaten metadaten quality repositorium
%T Towards a Data Quality Framework for Heterogeneous Data
%U http://hdl.handle.net/10454/12323
@inproceedings{micic2017towards,
added-at = {2018-08-03T17:06:54.000+0200},
author = {Micic, Natasha and Neagu, Daniel and Campean, I. Felician and Habib Zadeh, Esmaeil},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2b95ec9af14cbfbf6bbb9694ffb154438/hermann},
interhash = {5b943ac177cea14b8d4e74e449013de6},
intrahash = {b95ec9af14cbfbf6bbb9694ffb154438},
keywords = {forschungsdaten metadaten quality repositorium},
note = {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.},
timestamp = {2018-08-03T15:06:54.000+0200},
title = {Towards a Data Quality Framework for Heterogeneous Data},
url = {http://hdl.handle.net/10454/12323},
year = 2017
}
