The large amount of data generated every day makes necessary the re-implementation of new methods capable of handle with massive data efficiently. This is the case of Association Rules, an unsupervised data mining tool capable of extracting information in the form of IF-THEN patterns. Although several methods have been proposed for the extraction of frequent itemsets (previous phase before mining association rules) in very large databases, the high computational cost and lack of memory remains a major problem to be solved when processing large data. Therefore, the aim of this paper is three fold: (1) to review existent algorithms for frequent itemset and association rule mining, (2)to develop new efficient frequent itemset Big Data algorithms using distributive computation, as well as a new association rule mining algorithm in Spark, and (3) to compare the proposed algorithms with the existent proposals varying the number of transactions and the number of items. To this purpose, we have used the Spark platform which has been demonstrated to outperform existing distributive algorithmic implementations.

The internet use among children and adolescents has increased massively recently. This situation has promoted harmful situations such as cyberbullying, which is becoming a worldwide problem that entails serious consequences for well-being. The detection of these attitudes is essential to prevent and act accordingly. Groundbreaking techniques based on deep learning, like pre-trained language models, have achieved state-of-the-art results in many downstream Natural Language Processing tasks. This paper presents a simple but effective approach to improve the detection of cyberbullying situations by fine-tuning a pre-trained sentence transformer language model. We experimented on three datasets, and the results surpassed the state-of-the-art results. The approach could help prevent cyberbullying, filter those messages, and detect those children involved in the situation, providing opportunities to develop intervention programs to address this problem.

Despite the increasing capabilities of information technologies for data acquisition and processing, building energy management systems still require manual configuration and supervision to achieve optimal performance. Model predictive control (MPC) aims to leverage equipment control – particularly heating, ventilation and air conditioning (HVAC)– by using a model of the building to capture its dynamic characteristics and to predict its response to alternative control scenarios. Usually, MPC approaches are based on simplified linear models, which support faster computation but also present some limitations regarding interpretability, solution diversification and longer-term optimization. In this work, we propose a novel MPC algorithm that uses a full-complexity grey-box simulation model to optimize HVAC operation in non-residential buildings. Our system generates hundreds of candidate operation plans, typically for the next day, and evaluates them in terms of consumption and comfort by means of a parallel simulator configured according to the expected building conditions (weather, occupancy, etc.) The system has been implemented and tested in an office building in Helsinki, both in a simulated environment and in the real building, yielding energy savings around 35% during the intermediate winter season and 20% in the whole winter season with respect to the current operation of the heating equipment.