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Automatic Root Cause Analysis solutions aid analysts in finding problems’ root causes by using automatic data analysis. When trying to locate the root cause of a problem in a manufacturing process, an issue-denominated overlap can occur. Overlap can impede automated diagnosis using algorithms, as the data make it impossible to discern the influence of each machine on the quality of products. This paper proposes a new measure of overlap based on an information theory concept called Positive Mutual Information. This new measure allows for a more detailed analysis. A new approach is developed for automatically finding the root causes of problems when overlap occurs. A visualization that depicts overlapped locations is also proposed to ease practitioners’ analysis. The proposed solution is validated in simulated and real case-study data. Compared to previous solutions, the proposed approach improves the capacity to pinpoint a problem’s root causes.

Implementation of information and communication technologies (ICTs) in education is defined as the incorporation of ICTs into teaching and learning activities, both inside and outside the classroom. Despite widely studied, there is still no consensus on how it affects student performance. However, before evaluating this, it is crucial to identify which factors impact students' use of ICT for educational purposes. This understanding can help educational institutions to effectively implement ICT, potentially improving student results. Thus, adapting the conceptual framework proposed by Biagi and Loi (2013) and using the 2018 database of the Program for International Student Assessment (PISA) and a decision tree classification model developed based on CRISP-DM framework, we aim to determine which socio-demographic factors influence students' use of ICT for educational purposes. First, we categorized students according to their use of ICT for educational purposes in two situations: during lessons and outside lessons. Then, we developed a decision tree model to distinguish these categories and find patterns in each group. The model was able to accurately distinguish different levels of ICT adoption and demonstrate that ICT use for entertainment and ICT access at school and at home are among the most influential variables to predict ICT use for educational purposes. Moreover, the model showed that variables related to teaching best practices of Internet utilization at school are not significant predictors of such use. Some results were found to be country-specific, leading to the recommendation that each country adapts the measures to improve ICT use according to its context.

This paper describes a study on applying data mining techniques to power transformer failure prediction. The data set used consisted not only on DGA tests, but also in other tests done to the transformer’s insulating oil. This dataset presented several challenges, such as highly imbalanced classes (common in failure prediction problems), and the temporal nature of the observations. To overcome these challenges, several techniques were applied for prediction and better understand the dataset. Pre-processing and temporality incorporation in the dataset is discussed. For prediction, a 1-class and 2-class SVM, decision trees and random forests, as well as a LSTM neural network were applied to the dataset. As the prediction performance was low (high false-positive rate), we conducted a test to ascertain if the amount of data collected was sufficient. Results indicate that the frequency of data collection was not adequate, hinting that the degradation period was shorter than the periodicity of data collection.