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Abstract
The purpose of this paper is to develop a robust methodology to assess municipalities' performance concerning the consumption of resources in residential buildings. The assessment is carried out at a municipal level to inform decision makers about the relative position of their municipalities compared to others. In addition, the factors associated to better levels of municipal performance are identified, and the extent of their effects is quantified. The study uses an enhanced stochastic frontier panel model based on data of energy, water and materials consumption in Lisbon municipalities during the period 2003-2009. The study reveals that the municipalities' performance has remained stable over the years, although there are considerable differences in performance among municipalities. In addition, it is concluded that municipal performance tends to improve with the environmental policy expenditure and scale size, and decline with buildings' age, population density and the proportion of buildings with private ownership.

Abstract
Os casos de estudo sobre organizações e empresas são um veículo de comunicação de excelência na área da gestão. Este livro reúne uma série de casos de estudo que abordam a inovação e a diferenciação, a internacionalização, o marketing, a evolução estratégica, os modelos de negócio (e como são afetados pela tecnologia), as aquisições de empresas, e tem ainda um caso de estudo sobre a responsabilidade social (área de crescente importância para todo o tipo de organizações). A estratégia e o marketing são áreas de saber muito próximas, sendo dadas em conjunto em várias escolas de negócio no mundo inteiro. (...)

Abstract

Abstract
Scientific data analyses often apply a pipelined sequence of computational tasks to independent datasets. Each task in the pipeline captures and processes a dataset element, may be dependent on other tasks in the pipeline, may have a different computational complexity and may be filtered out from progressing in the pipeline. The goal of this work is to develop an efficient scheduler that automatically (i) manages a parallel data reading and an adequate data structure creation, (ii) adaptively defines the most efficient order of pipeline execution of the tasks, considering their inter-dependence and both the filtering out rate and the computational weight, and (iii) manages the parallel execution of the computational tasks in a multicore system, applied to the same or to different dataset elements. A real case study data analysis application from High Energy Physics (HEP) was used to validate the efficiency of this scheduler. Preliminary results show an impressive performance improvement of the pipeline tuning when compared to the original sequential HEP code (up to a 35x speedup in a dual 12-core system), and also show significant performance speedups over conventional parallelization approaches of this case study application (up to 10x faster in the same system).

Abstract