Abstract
Cardiotocography is widely used, all over the world, for fetal heart rate and uterine contractions monitoring before (antepartum) and during (intrapartum) labor, regarding the detection of fetuses in danger of death or permanent damage. However, analysis of cardiotocogram tracings remains a large and unsolved issue. State-of-the-art monitoring systems provide quantitative parameters that are difficult to assess by the human eye. These systems also trigger alerts for changes in the behavior of the signals. However, they usually take up to 10 min to detect these different behaviors. Previous work using machine learning for concept drift detection has successfully achieved faster results in the detection of such events. Our aim is to extend the monitoring system with memory-less fading statistics, which have been successfully applied in drift detection and statistical tests, to improve detection of alarming events.

Abstract
Clinical record integration and visualization is one of the most important abilities of modern health information systems (HIS). Its use on clinical encounters plays a relevant role in the efficacy and efficiency of healthcare. However, integrated HIS of central hospitals may gather millions of clinical reports (e.g. radiology, lab results, etc.). Hence, the clinical record must manage a stream of reports being produced in the entire hospital. Moreover, not all documents from a patient are relevant for a given encounter, and therefore not visualized during that encounter. Thus, the HIS must also manage a stream of events of visualization of reports, which runs in parallel to the stream of documents production. The aim of our project is to provide the physician with a recommendation of clinical reports to consider when they log in the computer. Our approach is to model relevance as the probability that a given document will be accessed in the current time frame. For that, we design a data stream management system to process the two streams, and Bayesian networks to learn those probabilities based on document, patient, department and user information. One of the biggest challenges to the learning problem, so far, is that no negative examples are produced by the stream (i.e. there are no record of documents not being visualized) leading to a one-class classification problem. The aim of this paper is to clearly present the setting and rationale for the approach. Current work is focused on both the stream processing mechanism and the Bayesian probability estimation.

Abstract

Abstract
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that continuously evolve over time, run in resource-aware environments, and detect and react to changes in the environment generating data. One important issue, not yet conveniently addressed, is the design of experimental work to evaluate and compare decision models that evolve over time. In this paper we propose a general framework for assessing the quality of streaming learning algorithms. We defend the use of Predictive Sequential error estimates over a sliding window to assess performance of learning algorithms that learn from open-ended data streams in non-stationary environments. This paper studies properties of convergence and methods to comparatively assess algorithms performance. Copyright 2009 ACM.

Abstract

Abstract
Smart grids consist of millions of automated electronic meters that will be installed in electricity distribution networks and connected to servers that will manage grid supervision, billing and customer services. World sustainability regarding energy management will definitely rely on such grids, so smart grids need also to be sustainable themselves. This sustainability depends on several research problems that emerge from this new setting (from power balance to energy markets) requiring new approaches for knowledge discovery and decision support. This paper presents a holistic distributed stream clustering view of possible solutions for those problems, supported by previous research in related domains. The approach is based on two orthogonal clustering algorithms, combined for a holistic clustering of the grid. Experimental results are included to illustrate the benefits of each algorithm, while the proposal is discussed in terms of application to smart grid problems. This holistic approach could be used to help solving some of the smart grid intelligent layer research problems, thus improving global sustainability.