From Prediction of the Improvement of the Quality towards an Equitable Sharing of the Cost of the Improvement between Business Processes
Jaouad Maqboul1, Bouchaib Bounabat2
1Jaouad Maqboul, Head of Software Engineering Department, Avenue Mohamed Ben Abdellah Regragui, Rabat, Morocco.
2Bouchaib Bounabat, Head of Software Engineering Department, Avenue Mohamed Ben Abdellah Regragui, Rabat, Morocco.
Manuscript received on January 21, 2021. | Revised Manuscript received on January 27, 2021. | Manuscript published on January 30, 2021. | PP: 268-274 | Volume-9 Issue-5, January 2021. | Retrieval Number: 100.1/ijrte.E5298019521 | DOI: 10.35940/ijrte.E5298.019521
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this work we have developed a quality approach for the quality assessment of data related to the business process for quality projects, this approach uses the cost of the implementation of quality combined with the impact of quality broken down into the benefit and efficiency of data, shapley value helps us choose the business processes that will collaborate to reduce the cost of improvement, Deep learning helps us calculate the quality values for any dimension based on history of previous improvements. To reach our goal, we used the cost-benefit approach (ACB) and the cost-effective approach (ACE) to extract the impact and cost factors then using a multi-optimization algorithm. -objective we will minimize the cost and maximize the impact for each business process and the deep learning introduced will complement our approach to learn from the previous improvements after validation of the processes which will be chosen as well as the values calculated after improvement. The importance of this research lies in the use of impact factors and the cost of the quality evaluation which represent the basis of any improvement, our approach uses generic multi-objective optimization algorithms which will help choose the minimum value of each business process before the improvement, adding a layer of predicting and estimating the quality value of the data generated by the business process before the improvement even, while the value of shapley has aim to minimize the cost of quality projects during fission and merger of companies and even within a company composed of several services and departments to have the lowest possible total cost to help companies manage the portfolios of quality.
Keywords: Artificial neural network, data quality assessment, data quality improvement, deep learning, prediction of improvement in data completeness shapley value.