In this work is discussed a case study of a business intelligence-BI-platform developed within the framework of an industry project by following research and development-R&D-guidelines of 'Frascati'. The proposed results are a part of the output of different jointed projects enabling the BI of the industry ACI Global working mainly in roadside assistance services. The main project goal is to upgrade the information system, the knowledge base-KB-and industry processes activating data mining algorithms and big data systems able to provide gain of knowledge. The proposed work concerns the development of the highly performing Cassandra big data system collecting data of two industry location. Data are processed by data mining algorithms in order to formulate a decision making system oriented on call center human resources optimization and on customer service improvement. Correlation Matrix, Decision Tree and Random Forest Decision Tree algorithms have been applied for the testing of the prototype system by finding a good accuracy of the output solutions. The Rapid Miner tool has been adopted for the data processing. The work describes all the system architectures adopted for the design and for the testing phases, providing information about Cassandra performance and showing some results of data mining processes matching with industry BI strategies.

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We propose in this work a study of an image processing engine able to detect automatically the features of electronic board weldings. The engine has been developed by using ImageJ and OpenCV libraries. Specifically the image processing segmentation has been improved by watershed approach. After a complete design of the automation processes, different test have been performed showing the engine efficiency in terms of features extraction, scale setting and thresholding calibration. The engine provides as outputs the storage of the cropped images of each single defects. The proposed engine together with the post-processing 3D imaging represent a good tool for the management of the production quality of electronic boards.

In this paper the authors proposed different Multilayer Perceptron Models (MLP) of artificial neural networks (ANN) suitable for visual merchandising in Global Distribution (GDO) applications involving supermarket product facing. The models are related to the prediction of different attributes concerning mainly shelf product allocation applying times series forecasting approach. The study highlights the range validity of the sales prediction by analysing different products allocated on a testing shelf. The paper shows the correct procedures able to analyse most guaranteed results, by describing how test and train datasets can be processed. The prediction results are useful in order to design monthly a planogram by taking into account the shelf allocations, the general sales trend, and the promotion activities. The preliminary correlation analysis provided an innovative key reading of the predicted outputs. The testing has been performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning process of the model. The proposed study is developed within a framework of an industry project.

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