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This paper describes a performance assessment of Portuguese secondary schools using data envelopment analysis (DEA). The assessment adopts a perspective where schools are viewed as promoting students achievement given their characteristics in terms of academic abilities and socio-economic background. Our sample comprised all secondary schools in Portugal with both basic and secondary education levels. Two types of DEA analysis are performed: one using an output-oriented model that restricts output (exam scores) weights to be linked to the number of students that have done that exam in the school, and the other using a model that restricts factor weights to be equal for all schools. In this model the weight restrictions are linked to the total number of exams done nationally. The first model is well suited for identifying worst performing schools and to assess schools that may specialize in certain subjects, whereas the latter is best suited for improving discrimination between best performing schools when pursuing the identification of benchmarks, as well as to construct performance rankings. Journal of the Operational Research Society (2012) 63, 1098-1115. doi: 10.1057/jors.2011.114 Published online 16 November 2011

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The purpose of the paper is to examine trends in the performance of the Portuguese construction industry, and identify the factors that promote excellence and innovation in the sector. From a methodological perspective, this study enhances the construction of composite indicators using the principles of the "benefit of the doubt" weighting. This involves the use of Data Envelopment Analysis to estimate weights for aggregating the key performance indicators of the construction companies. The paper also proposes a new DEA-based method to assess innovation within an industry, identifying the innovative companies and the extent of innovation. The determinants of good performance and innovation are examined using regression techniques and the statistical significance of the results is ensured by the use of bootstrapping. The study reveals that the Portuguese construction industry experienced a remarkable performance improvement during the 1990s, but this growth trend in performance slowed down in recent years. It is concluded that company performance is strongly affected by the national economic context, and that small specialized companies and large contractors tend to achieve the best performance levels.

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Mining public transportation networks is a growing and explosive challenge due to the increasing number of information available. In highly populated urban zones, the vehicles can often fail the schedule. Such fails cause headway deviations (HD) between high-frequency bus pairs. In this paper, we propose to identify systematic HD which usually provokes the phenomenon known as Bus Bunching (BB). We use the PrefixSpan algorithm to accurately mine sequences of bus stops where multiple HD frequently emerges, forcing two or more buses to clump. Our results are promising: 1) we demonstrated that the BB origin can be modeled like a sequence mining problem where 2) the discovered patterns can easily identify the route schedule points to adjust in order to mitigate such events.

Abstract
In highly populated urban zones, it is common to notice headway deviations (HD) between pairs of buses. When these events occur in a bus stop, they often cause bus bunching (BB) in the following bus stops. Several proposals have been suggested to mitigate this problem. In this paper, we propose to find BBS (Bunching Black Spots) - sequences of bus stops where systematic HD events cause the formation of BB. We run a sequence mining algorithm, named PrefixSpan, to find interesting events available in time series. We prove that we can accurately model the BB trip usual pattern like a frequent sequence mining problem. The subsequences proved to be a promising way of identify the route' schedule points to adjust in order to mitigate such events. © 2012 Springer-Verlag.