Anomaly detection is a machine learning task that has been investigated within diverse research areas and application domains. In this paper, we performed anomaly detection for air pollution and public transport traffic analysis for the city of Oslo, Norway. To this aim, the state-of-the-art method SparkGHSOM was considered to learn predictive models for normal (i.e. regular) scenarios of air quality and traffic jams in a distributed fashion. Furthermore, we extended the main algorithm to make the detected anomalies explainable through an instance-based feature ranking approach. The results showed that SparkGHSOM is able to detect anomalies for both the real applications considered in this study, despite the fact it was designed for different tasks.

Transfer learning focuses on enhancing predictive models for a target domain, by exploiting the knowledge coming from a related source domain. However, most existing transfer learning methods assume that source and target domains are described with the same feature spaces. Heterogeneous transfer learning approaches aim to overcome this limitation, but they usually introduce strong assumptions (e.g., on the number of features), cannot distribute the workload to handle large volumes of data, or cannot work in challenging settings like the Positive-Unlabeled (PU) setting, where only positive and unlabelled examples are available. In this paper, we present a novel heterogeneous distributed transfer learning method that can work also in PU learning setting and overcomes all such limitations.The experimental evaluation was conducted in the context of a link prediction task in the biological domain. The results showed the effectiveness of the proposed method, that outperformed three state-of-the-art heterogeneous transfer learning approaches.