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

Abstract
This paper presents an analysis of the impacts of photovoltaics and storage units for self-consumption in the day-ahead spot prices. A methodology is proposed, to access these impacts in the Iberian electricity market for 2015, 2020 and 2030.

Abstract
A large amount of duplicate data typically exists across volumes of virtual machines in cloud computing infrastructures. Deduplication allows reclaiming these duplicates while improving the cost-effectiveness of large-scale multitenant infrastructures. However, traditional archival and backup deduplication systems impose prohibitive storage overhead for virtual machines hosting latency-sensitive applications. Primary deduplication systems reduce such penalty but rely on special cluster filesystems, centralized components, or restrictive workload assumptions. Also, some of these systems reduce storage overhead by confining deduplication to off-peak periods that may be scarce in a cloud environment. We present DEDIS, a dependable and fully decentralized system that performs cluster-wide off-line deduplication of virtual machines' primary volumes. DEDIS works on top of any unsophisticated storage backend, centralized or distributed, as long as it exports a basic shared block device interface. Also, DEDIS does not rely on data locality assumptions and incorporates novel optimizations for reducing deduplication overhead and increasing its reliability. The evaluation of an open-source prototype shows that minimal I/O overhead is achievable even when deduplication and intensive storage I/O are executed simultaneously. Also, our design scales out and allows collocating DEDIS components and virtual machines in the same servers, thus, sparing the need of additional hardware.