Abstract
We present two methods for testing the predictive power of a variable length Markov chain induced from a collection of user web navigation sessions. The collection of sessions is split into a training and a test set. The first method uses a chi(2) statistical test to measure the significance of the distance between the distribution of the probabilities assigned to the test trails by a Markov model build from the full collection of sessions and a model built from the training set. The statistical test measures the ability of the model to generalise its predictions to the unseen sessions from the test set. The second method evaluates the model ability to predict the last page of a navigation session based on the preceding pages viewed by recording the mean absolute error of the rank of the last occurring page among the predictions provided by the model. Experimental results conducted on both real and random data sets are reported and the results show that in most cases a second-order model is able to capture sufficient history to predict the next link choice with high accuracy.

Abstract
Markov models have been widely used for modelling users' web navigation behaviour. In previous work we have presented a dynamic clustering-based Markov model that accurately represents second-order transition probabilities given by a collection of navigation sessions. Herein, we propose a generalisation of the method that takes into account higher-order conditional probabilities. The method makes use of the state cloning concept together with a clustering technique to separate the navigation paths that reveal differences in the conditional probabilities. We report on experiments conducted with three real world data sets. The results show that some pages require a long history to understand the users choice of link, while others require only a short history. We also show that the number of additional states induced by the method can be controlled through a probability threshold parameter.

Abstract
In BSc/MSc engineering programmes at Faculty of Engineering of the University of Porto (FEUP), the need to provide students with teamwork experiences close to a real world environment was identified as an important issue. A new group-formation method that aims to provide an enriching teamwork experience is proposed. Students are asked to answer a questionnaire to evaluate their teamwork profiles and are assigned to groups by an algorithm aiming to achieve maximum diversity within groups and homogeneity among groups. The profile diversity/complementarity within a group is an important factor to promote members' commitment and coordination in order to achieve the proposed goals. The proposed method is compared to a standard self-selection method for three engineering programmes in three academic years. The results show that, with the new method, there are a higher number of medium ranked groups which surpass the expectation and that, contrary to some students' beliefs, the method does not have a negative impact on the overall final marks. © 2009 SEFI.

Abstract
Hospital performance is a critical issue in society and its assessment must be tactfully studied in order to evaluate future decisions. In this paper we report an operating theatre study based on a simulation model, describing one of the biggest public hospitals in the north of Portugal. The model encompasses several operating rooms shared among different medical services and considers the arrival of both elective and emergency patients. We focus on a critical planning problem of the operating theatre, the allocation of medical services to operating rooms and shifts. With a discrete-event simulation model we assess the performance of the current schedule distribution and perform a set of tests in order to find a better master surgery schedule. Experiments show improvement opportunities by balancing surgical services capacities. © 2011 IEEE.

Abstract
From lone to short term planning, the decision processes inherent to surgery theatre organization are often subject of empiricism. The current hospital information systems available on Portuguese public hospitals lack a decision support system component that could help achieve better planning solutions, thus better operational performance of the surgery theatre. Since the surgery theatre is the biggest hospital budget consumer, the use of surgery related resources and its intrinsic planning must be carefully studied. We developed a new decision support system for surgery planning conjointly with one of the largest hospitals in the north of Portugal. As for now, the goals of the DSS are to improve the planning process and increase policy compliance. We will enhance this framework by integrating data mining, optimization and simulation techniques in a way that enables a more accurate representation of the surgery theatre problems' stochastic nature, allowing the users to rind enhanced planning alternatives.

Abstract
This paper presents a combination of optimization and data mining techniques to address the surgery scheduling problem. In this approach, we first develop a model to predict the duration of the surgeries using a data mining algorithm. The prediction model outcomes are then used by a mathematical optimization model to schedule surgeries in an optimal way. In this paper, we present the results of using three different data mining algorithms to predict the duration of surgeries and compare them with the estimates made by surgeons. The results obtained by the data mining models show an improvement in estimation accuracy of 36%.We also compare the schedules generated by the optimization model based on the estimates made by the prediction models against reality. Our approach enables an increase in the number of surgeries performed in the operating theater, thus allowing a reduction on the average waiting time for surgery and a reduction in the overtime and undertime per surgery performed. These results indicate that the proposed approach can help the hospital improve significantly the efficiency of resource usage and increase the service levels. © Springer-Verlag 2012.