Michelangelo Ceci

Relational Data Mining in the Era of Big Data

The aim of this article is to synthetically describe a sample of distinct approaches and applications of Relational Data Mining, which address the issue of managing complex, and possibly big, amounts of data. Specifically, we report a brief review of the literature on Relational Data Mining in the fields of Spatial Data Mining, Process Mining, Network Data Analysis and Stream Data Mining, with an emphasis on the Italian research. For each field, we describe the milestones that have been reached, as well as the future research trends that are fuelled by the emergent ubiquity of Big Data.

Mining Spatio-Temporal Patterns of Periodic Changes in Climate Data

The climate changes have attracted always interest because they may have great impact on the life on Earth and living beings. Computational solutions may be useful both for the prediction of the climate changes and for their characterization, perhaps in association with other phenomena. Due to the cyclic and seasonal nature of many climate processes, studying their repeatability may be relevant and, in many cases, determinant. In this paper, we investigate the task of determining changes of the weather conditions, which are periodically repeated over time and space. We introduce the spatio-temporal patterns of periodic changes and propose a computational solution to discover them. These patterns allows us to represent spatial regions with same periodic changes. The method works on a grid-based data representation and relies on a time-windows analysis model to detect periodic changes in the grid cells. Then, the cells with same changes are selected to form a spatial region of interest. The usefulness of the method is demonstrated on a real-world dataset collecting weather conditions.

User-Emotion Detection Through Sentence-Based Classification Using Deep Learning: A Case-Study with Microblogs in Albanian

Human emotion analysis has always stimulated studies in different disciplines, such as Cognitive Sciences, Psychology, and thanks to the diffusion of the social media, it is attracting the interests of computer scientists too. Particularly, the growing popularity of Microblogging platforms, has generated large amounts of information which in turn represent an attractive source of data to be further subjected to opinion mining and sentiment analysis. In our research, we leverage the analysis performed on micro-blogging texts and postings in Albanian language, which enables the use of technologies to monitor and follow the feelings and perception of the people with respect to products, issues, events, etc. Our approach to emotion analysis tackles the problem of classifying a text fragment into a set of pre-defined emotion categories and therefore aims at detecting the emotional state of the writer conveyed through the text. In order to achieve this goal, we perform a comparative analysis between different classifiers, using deep learning and other classical machine learning classification algorithms. We also adopt a domestic stemming tool for Albanian language in order to preprocess the datasets used in a second round of experiments. Experimental evaluation shows that deep learning produces overall better results compared with the other methods in terms of classification accuracy. We present also other findings related to the length of the texts being processed and the impact on the classifiers’ accuracy.

An Empirical Evaluation of Sequential Pattern Mining Algorithms

Sequence mining is one of the most investigated tasks in data mining and it has been studied under several perspectives. With the rise of Big Data technologies, the perspective of efficiency becomes prominent especially when mining massive sequences. In this paper, we perform a thorough experimental evaluation of several algorithms for sequential pattern mining and we provide an analysis of the results focusing on the different algorithmic choices and how these affect the performance of each algorithm. Experiments performed on real-world and synthetic datasets highlight relevant differences between existing algorithms and provide indications for Big Data scenarios.