Abstract
Education covers a range of sectors from kindergarten to higher education. In the education system, each grade has three possible outcomes: dropout, retention and pass to the next grade. In this work, we study the data from the Department of Statistics of Education and Science (DGEEC) of the Education Ministry. DGEEC maintains those outcomes for each school year, therefore, this study seeks a longitudinal view based on student flow. The document reports the data pre-processing, a stochastic model based on the pre-processed data and a data generation process that uses the previous model. © 2020, Springer Nature Switzerland AG.

Abstract
The increase in retention of customers in gyms and health clubs is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused essentially on predictive analytics, neglecting the business domain. This work presents an actionable knowledge discovery system that uses the following pipeline (data collection, predictive model and retention interventions). In the first step, it extracts and transforms existing real data from databases of the sports facilities. In the second step, predictive models are applied to identify user profiles more susceptible to dropout, where actionable withdrawal rules are based on actionable attributes. Finally, in the third step, based on the previous actionable knowledge, some of the values of the actionable attributes should be changed in order to increase retention. Simulation of scenarios is carried out, with test and control groups, where business utility and associated cost are measured. This document presents a bi-objective study in order to choose the more efficient scenarios.

Abstract
Star schema in data warehouses is a very well established model. However, the increasing number of star schemas creating large constellations schemas add new challenges in the organizations. In this document, we intend to make a contribution in the technical architecture of data warehouses with constellation schemas using an extension of the bus matrix. The proposed supply-demand matrix details the raw data from the original databases, describes the constellation schemas with different dimensions and establishes the information demand requirements. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper tackles problems where attribute selection is not only able to choose a few features but also to achieve a low performance classification in terms of accuracy compared to the full attribute set. Correlation-based feature selection (CFS) has been set as the baseline attribute subset selector due to its popularity and high performance. Around hundred data sets have been collected and submitted to CFS; then the problems fulfilling simultaneously the conditions: (a) a number of selected attributes lower than six and (b) a percentage of selected attributes lower than a forty per cent, have been tested onto two directions. Firstly, in the scope of data selection at the feature level, an advanced contemporary approach have been conducted as well as some options proposed in a prior work. Secondly, the pre-processed and initial problems have been tested with some sturdy classifiers. Moreover, this work introduces a new taxonomy of feature selection according to the solution type and the followed way to compute it. The test bed comprises seven problems featured by a low dimensionality after the CFS application, three out of them report a single selected attribute, another one with two extracted features and the three remaining data sets with four or five retained attributes; additionally, the initial feature set is between six and twenty-nine and the complexity of the problems, in terms of classes, fluctuates between two and twenty-one, throwing averages of sixteen and around five for both aforementioned properties. The contribution concluded that the advanced procedure (extended CFS) is suitable for problems where only one or two attributes are selected by CFS; for data sets with more than two selected features the baseline method is preferable to the advanced one, although the considered feature ranking method achieved intermediate results.

Abstract

Abstract
Recommendation systems try to guide the users in carrying out a task providing them with useful information about it. Considering the context of software development, programs are ever-increasing, making it difficult to conduct a detailed verification and validation. Automated static analyzers help to detect possible faults on software products earlier and quickly but, in general, the issue maybe a false-positive warning. In this sense, this work presents and evaluates a recommendation system, called WarningsFIX (WFX), which combines several static analyzers aim at: i) Expand the possible fault domain approached by each static analysis tool increasing the range of warnings types covered, allowing the concentration of a higher number of true-positive warnings. ii) Establish different prioritization strategies of warnings aiming at suggesting for reviewers first analyze the ones with a higher chance of being true-positive. WFX organizes the warnings information via treemaps considering four levels of abstraction: program, package, class, and line the nodes of the treemap on each level may be classified by three different prioritization strategies based on the number of warnings, the number of tools, and the suspicions rate the use of these strategies enables the reviewer to handle the set of warnings in a coordinated way depending on the cost and time constraint available. We perform a feasibility study to evaluate the WFX effectiveness whose results shown that: i) WFX was able to improve the results obtained from combined static analyzers to 44% of the analyzed programs, concentrating for them a greater number of true-positives. ii) WFX, depending on the adopted prioritization strategy, improved from 67.5% to 55% the ranking of lines with real bugs when compared with the list of warnings provided by the automated static analyzers without the WFX support.