Data Completeness Prediction by Deep Learning

Proceedings of ‏The 3rd International Conference on Modern Research in Engineering, Technology & Science

Year: 2020


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Data Completeness Prediction by Deep Learning

Jaouad MAQBOUL, Bouchaib Bounabat



This article describes an approach to identify the tangible and intangible impact of better data quality, in an enterprise architecture context without for-getting the cost resulting from the improvement of this data. the goal is to measure the impact and cost of improving business processes, quantitatively, to help decision-makers make good decisions and carry out their strategy, this approach will facilitate the choice of candidate quality projects to be executed by minimize cost of improvement, an JEE java web application is developed to meet our need.

Keywords: Data quality assessment and improvement, artificial neural network, deep learning Introduction.