Abstract

Abstract
The Biblioteca Digital de Arte (BDArt) Digital Library hosted by the Thematic Repository at the University of Porto (Repositório Tem ático da U.Porto) aggregates documents from the library and the archive collections belonging to the Fine Arts School of the University of Porto (Faculdade de Belas Artes da U.Porto). This school has a museum collection containing a significant set of world-class ob- jects managed with distinct processes and tools from those currently used in libraries and archives elsewhere. Interoperability between the collections of the archive, the library, and the museum is necessary because many works allocated to different collections are closely related and can only be seen as a whole by cross-collection search functionalities. The goal of this work, the first of its kind to be developed at the University of Porto (U. Porto), is to integrate the museum collection with archives and library collections in the repository and to use an open-source technology (DSpace). Our experiment involved the selection of appropriate representations of the objects and the definition of a metadata crosswalk between the original metadata standards and qualified Dublin Core. As a result, we created the BDA Museum Collection as a BDArt subcom- munity using an XML export procedure that we expect to be helpful in future developments of other museum collections in the Thematic Repository at U.Porto. © Isabel Barroso, Nadia Hartmann, and Cristina Ribeiro.

Abstract
This study develops a framework to provide insights regarding the performance of the farms of an energy player in the Portuguese wind sector. The focus of the wind farm performance assessment is on the operating stage which corresponds to the electrical energy generation process, during 2010 and 2011. In a first stage, Data Envelopment Analysis is used to measure the efficiency of wind farms in generating electrical energy from the resources available and non-discretionary variables. This analysis enables the identification of the best practices of the efficient farms which can be emulated by inefficient ones. In a second stage, changes in wind farms productivity are investigated using Malmquist index. Bootstrap procedures are applied to obtain statistical inference on the efficiency estimates. We conclude that almost all farms decreased overall productivity levels, mainly due to the decline in the productivity levels of the frontier, which is in accordance with the decrease in wind availability observed in 2011.

Abstract
In this work, we introduce the MuSer, a propositional framework that explores temporal information available in multi-relational databases. At the core of this system is an encoding technique that translates the temporal information into a propositional sequence of events. By using this technique, we are able to explore the temporal information using a propositional sequence miner. With this framework, we mine each class partition individually and we do not use classical aggregation strategies, like window aggregation. Moreover, in this system we combine feature selection and propositionalization techniques to cast a multi-relational classification problem into a propositional one. We empirically evaluate the MuSer framework using two real databases. The results show that mining each partition individually is a time-and memory-efficient strategy that generates a high number of highly discriminative patterns.

Abstract
Automatic vision systems are widely used in sports competition to analyze individual and collective performance during the matches. However, the complex implementation based on multiple fixed cameras and the human intervention on the process makes this kind of systems expensive and not suitable for the big majority of the teams. In this paper we propose a low-cost, portable and flexible solution based on the use of Unmanned Air Vehicles to capture images from indoor soccer games. Since these vehicles suffer from vibrations and disturbances, the acquired video is very unstable, presenting a set of unusual problems in this type of applications. We propose a complete video-processing framework, including video stabilization, camera calibration, player detection, and team performance analysis. The results showed that camera calibration was able to correct automatically image-to-world homography; the player detection precision and recall was around 75%; and the high-level data interpretation showed a strong similarity with ground-truth derived results.

Abstract
Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, which can then be used to direct the execution of other applications. The resulting values are derived by the distributed computation of functions like COUNT, SUM, and AVERAGE. Some application examples deal with the determination of the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.