An overview of recent distributed algorithms for learning fuzzy models in Big Data classification

AbstractNowadays, a huge amount of data are generated, often in very short time intervals and in various formats, by a number of different heterogeneous sources such as social networks and media, mobile devices, internet transactions, networked devices and sensors. These data, identified as Big Data in the literature, are characterized by the popular Vs features, such as Value, Veracity, Variety, Velocity and Volume. In particular, Value focuses on the useful knowledge that may be mined from data. Thus, in the last years, a number of data mining and machine learning algorithms have been proposed to extract knowledge from Big Data. These algorithms have been generally implemented by using ad-hoc programming paradigms, such as MapReduce, on specific distributed computing frameworks, such as Apache Hadoop and Apache Spark. In the context of Big Data, fuzzy models are currently playing a significant role, thanks to their capability of handling vague and imprecise data and their innate characteristic to be interpretable. In this work, we give an overview of the most recent distributed learning algorithms for generating fuzzy classification models for Big Data. In particular, we first show some design and implementation details of these learning algorithms. Thereafter, we compare them in terms of accuracy and interpretability. Finally, we argue about their scalability

Adaptive management of applications across multiple clouds:the SeaClouds approach

How to deploy and manage, in an efficient and adaptive way, complex applications across multiple heterogeneous cloud platforms is one of the problems that have emerged with the cloud revolution. In this paper we present context, motivations and objectives of the EU research project SeaClouds, which aims at enabling a seamless adaptive multi-cloud management of complex applications by supporting the distribution, monitoring and migration of application modules over multiple heterogeneous cloud platforms. After positioning SeaClouds with respect to related cloud initiatives, we present the SeaClouds architecture and discuss some of its aspect, such as the use of the OASIS standard TOSCA and the compatibility with the OASIS CAMP initiative

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Study and design of multi-objective evolutionary fuzzy systems for improving the interpretability accuracy trade off of linguistic fuzzy rule based systems when dealing with high-dimensional and large scale problems

Tesis Univ. Granada. Programa Oficial de Doctorado en: Tecnología de la Información y la Comunicació

Progetto e realizzazione di un simulatore per l'analisi dei guasti tipici in un campo fotovoltaico

Data la recente diffusione dei sistemi fotovoltaici, rilevare e identificare i guasti in questi sistemi assume un ruolo sempre più importante per limitare le perdite economiche legate alla mancata produzione di energia. La rilevazione e la diagnosi dei guasti può essere effettuata utilizzando sistemi basati su tecniche di intelligenza computazionale; tali sistemi hanno bisogno di una fase iniziale di addestramento, in cui si presentano coppie di campioni ingresso-uscita desiderata; tali dati possono essere ottenuti facilmente utilizzando un simulatore fotovoltaico; i simulatori esistenti non si sono rivelati adatti in quanto non permettono una configurazione flessibile dei guasti, per cui in questo lavoro di tesi è stato progettato e sviluppato un nuovo simulatore di campo fotovoltaico che permette di simulare guasti di cortocircuito e circuito aperto su celle e disattivazione di intere stringhe e moduli. I test di validazione effettuati hanno confermato che il comportamento del simulatore coincide con quello descritto in letteratura, pertanto si ritiene che il simulatore possa essere utilizzato per generare i dati necessari all'addestramento di un sistema di rilevazione e diagnosi basato su tecniche di intelligenza computazionale. Due to the recent spread of photovoltaic (PV) systems, fault detection and recognition in these systems plays an increasingly important role to limit the economic losses related to power reduction. Fault detection and diagnosis can be achieved using computational intelligence systems; these systems require an inital training, which consists in presenting pairs of 'input'-'desidered output' samples; a PV simulator can produce these data easily; existing simulators are not suitable for this purpose because they do not allow proper configuration of faults; in this work we have designed and developed a new PV array simulator which allows short circuit and open circuit faults simulation and modules and strings disconnection. Validation tests have confirmed that the simulator behaviour is coherent with a number of works described in literature, so this simulator can be used to generate training data for a PV fault detection and diagnosis system based on computational intelligence

Incremental and Interpretable Learning Analytics Through Fuzzy Hoeffding Decision Trees

Artificial Intelligence-based methods have been thoroughly applied in various fields over the years and the educational scenario is not an exception. However, the usage of the so-called explainable Artificial Intelligence, even if desirable, is still limited, especially whenever we consider educational datasets. Moreover, the time dimension is not often regarded enough when analyzing such types of data. In this paper, we have applied the fuzzy version of the Hoeffding Decision Tree to an educational dataset, considering separately STEM and Social Sciences subjects, in order to take into consideration both the time evolution of the educational process and the possible interpretability of the final results. The considered models resulted to be successful in discriminating the passing or failing of exams at the end of consecutive semesters on the part of students. Moreover, Fuzzy Hoeffding Decision Tree occurred to be much more compact and interpretable compared to the traditional Hoeffding Decision Tree