Abstract
The current trend of increasingly larger Web-based applications makes scalability the key challenge when developing, deploying, and maintaining data centers. At the same time, the migration to the cloud computing paradigm means that each data center hosts an increasingly complex mix of applications, from multiple owners and in constant evolution. Unfortunately, managing such data centers in a cost-effective manner requires that the scalability properties of the hosted workloads to be accurately known, namely, to proactively provision adequate resources and to plan the most economical placement of applications. Obviously, stopping each of them and running a custom benchmark to asses its scalability properties is not an option. In this paper we address this challenge with a tool to measure the software scalability regarding CPU availability, to predict system behavior in face of varying resources and an increasing workload. This tool does not depend on a particular application and relies only on Linux's SystemTap probing infrastructure. We validate the approach first using simulation and then in an actual system. The resulting better prediction of scalability properties should allow improved (self-)management practices.

Abstract
In this work we intend to advance towards a computational model to hold up a Group Decision Support System for VirtualECare, a system aimed at sustaining online healthcare services, where Extended Logic Programs (ELP) will be used for knowledge representation and reasoning. Under this scenario it is possible to evaluate the ELPs making in terms of the Quality-of-Information (QoI) that is assigned to them, along the several stages of the decision making process, which is given as a truth value in the interval 0 ... 1, i.e., it is possible to provide a measure of the value of the QoI that supports the decision making process, an end in itself. It will be also considered the problem of QoI evaluation in a multicriteria decision setting, being the criteria to be fulfilled that of a Clinical Guideline (CG) for Chronic Obstructive Pulmonary Disease.

Abstract
We address the problem of providing transparent, lightweight, fault-tolerance mechanisms for generic peer-to-peer middleware systems. The main idea is to use the peer-to-peer overlay to provide for fault-tolerance rather than support it higher up in the middleware architecture, e.g. in the form of services. To evaluate our approach we have implemented a fault-tolerant middleware prototype that uses a hierarchical peer-to-peer overlay in which the leaf peers connect to sensors that provide data streams. Clients connect to the root of the overlay and request streams that are routed upwards through intermediate peers in the overlay up to the client. We report encouraging preliminary results for latency, jitter and resource consumption for both the non-faulty and faulty cases.

Abstract
Computer-assisted cardiac arrhythmia detection and classification can play a significant role in the management of cardiac disorders. In this paper, we propose a new approach for arrhythmia classification based on a combination of morphological and dynamic features. Wavelet Transform (WT) and Independent Component Analysis (ICA) are applied separately to each heartbeat to extract corresponding coefficients, which are categorized as 'morphological' features. In addition, RR interval information is also obtained characterizing the 'rhythm' around the corresponding heartbeat providing 'dynamic' features. These two different types of features are then concatenated and Support Vector Machine (SVM) is utilized for the classification of heartbeats into 15 classes. The procedure is applied to the data from two ECG leads independently and the two results are fused for the final decision. Compare the two classification results and the classification result is kept if the two are identical or the one with greater classification confidence is picked up if the two are inconsistent. The proposed method was tested over the entire MIT-BIH Arrhythmias Database [1] and it yields an overall accuracy of 99.66% on 85945 heartbeats, better than any other published results.

Abstract
The changes in the communication paradigm envisioned for next-generation networks (NGNs), with peer-to-peer/symmetric attachments gaining momentum and two Internet Protocol (IP) versions coexisting, will pose new challenges to mobile communication networks. Traditional IP auto-configuration mechanisms will not work properly, since they were designed mostly having in mind a client-server/asymmetric attachment model, they assume a single IP version paradigm, and they target the auto-configuration of devices only. The IST Ambient Networks (ANs) project has introduced a new concept the AN that enables handling every communication entity, either a single device or an entire network, as an AN. This paper describes a new efficient mechanism, named Basic Connectivity (BC) mechanism, for auto-configuring IP connectivity between attaching ANs. A proof-of-concept prototype, experimental results, and theoretical analysis show that BC suites the future networking paradigm and represents a solution more efficient than the current trial-and-error mechanism for auto-configuring IP connectivity. Copyright (C) 2009 John Wiley & Sons, Ltd.

Abstract
Many biological networks contain recurring overrepresented elements, called network motifs. Finding these substructures is a computationally hard task related to graph isomorphism. G-Tries are an efficient data structure, based on multiway trees, capable of efficiently identifying common substructures in a set of subgraphs. They are highly successful in constraining the search space when finding the occurrences of those subgraphs in a larger original graph. This leads to speedups up to 100 times faster than previous methods that aim for exact and complete results. In this paper we present a new efficient sampling algorithm for subgraph frequency estimation based on g-tries. It is able to uniformly traverse a fraction of the search space, providing an accurate unbiased estimation of subgraph frequencies. Our results show that in the same amount of time our algorithm achieves better precision than previous methods, as it is able to sustain higher sampling speeds.