Abstract
Data streams are usually characterized by changes in the underlying distribution generating data. Therefore algorithms designed to work with data streams should be able to detect changes and quickly adapt the decision model. Rules are one of the most interpretable and flexible models for data mining prediction tasks. In this paper we present the Adaptive Very Fast Decision Rules (AVFDR), an on-line, any-time and one-pass algorithm for learning decision rules in the context of time changing data. AVFDR can learn ordered and unordered rule sets. It is able to adapt the decision model via incremental induction and specialization of rules. Detecting local drifts takes advantage of the modularity of rule sets. In AVFDR, each individual rule monitors the evolution of performance metrics to detect concept drift. AVFDR prunes rules that detect drift. This explicit change detection mechanism provides useful information about the dynamics of the process generating data, faster adaption to changes and generates compact rule sets. The experimental evaluation shows this method is able to learn fast and compact rule sets from evolving streams in comparison to alternative methods. © 2012 Springer-Verlag.

Abstract
Vehicular sensing is emerging as a powerful mean to collect information using the variety of sensors that equip modern vehicles. These sensors range from simple speedometers to complex video capturing systems capable of performing image recognition. The advent of connected vehicles makes such information accessible nearly in real-time and creates a sensing network with a massive reach, amplified by the inherent mobility of vehicles. In this paper we discuss several applications that rely on vehicular sensing, using sensors such as the GPS receiver, windshield cameras, or specific sensors in special vehicles, such as a taximeter in taxi cabs. We further discuss connectivity issues related to the mobility and limited wireless range of an infrastructure-less network based only on vehicular nodes. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

Abstract
One of the major data mining tasks is to cluster similar data, because of its usefulness, providing means of summarizing large ammounts of raw data into handy information. Clustering data streams is particularly challenging, because of the constraints imposed when dealing with this kind of input. Here we report our work, in which it was investigated the use of WiSARD discriminators as primary data synthesizing units. An analysis of StreamWiSARD, a new sliding-window stream data clustering system, the benefits and the drawbacks of its use and a comparison to other approaches are all presented. © 2012 Springer-Verlag.

Abstract
This work presents an optimized version of XMuSer, an ILP based framework suitable to explore temporal patterns available in multi-relational databases. XMuSer's main idea consists of exploiting frequent sequence mining, an efficient method to learn temporal patterns in the form of sequences. XMuSer framework efficiency is grounded on a new coding methodology for temporal data and on the use of a predictive sequence miner. The frameworks selects and map the most interesting sequential patterns into a new table, the sequence relation. In the last step of our framework, we use an ILP algorithm to learn a classification theory on the enlarged relational database that consists of the original multi-relational database and the new sequence relation. We evaluate our framework by addressing three classification problems and map each one of three different types of sequential patterns: frequent, closed or maximal. The experiments show that our ILP based framework gains both from the descriptive power of the ILP algorithms and the efficiency of the sequential miners. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
This work addresses the problem of mining data stream generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnosis degradations of this process, using change detection mechanisms, and self-repairs the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learns can detect re-occurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models.

Abstract
In recent years we witnessed an impressive advance in the social networks field, which became a "hot" topic and a focus of considerable attention. Also, the development of methods that focus on the analysis and understanding of the evolution of data are gaining momentum. In this paper we present an approach to visualize the evolution of dynamic social networks by using Tucker decomposition and the concept of trajectory. Our visualization strategy is based on trajectories of network's actors in a bidimensional space that preserves its structural properties. Furthermore, this approach can be used to identify similar actors by comparing the shape and position of the trajectories. To illustrate the proposed approach we conduct a case study using a set of temporal friendship networks.