Abstract
The problem of predicting the next request during a user's navigation session has been extensively studied. In this context, higher-order Markov models have been widely used to model navigation sessions and to predict the next navigation step, while prediction accuracy has been mainly evaluated with the hit and miss score. We claim that this score, although useful, is not sufficient for evaluating next link prediction models with the aim of finding a sufficient order of the model, the size of a recommendation set, and assessing the impact of unexpected events on the prediction accuracy. Herein, we make use of a variable length Markov model to compare the usefulness of three alternatives to the hit and miss score: the Mean Absolute Error, the Ignorance Score, and the Brier score. We present an extensive evaluation of the methods on real data sets and a comprehensive comparison of the scoring methods.

Abstract
It is well known that the definition of bus schedules is critical for the service reliability of public transports. Several proposals have been suggested, using data from Automatic Vehicle Location (AVL) systems, in order to enhance the reliability of public transports. In this paper we study the optimum number of schedules and the days covered by each one of them, in order to increase reliability. We use the Dynamic Time Warping distance in order to calculate the similarities between two different dimensioned irregularly spaced data sequences before the use of data clustering techniques. The application of this methodology with the K-Means for a specific bus route demonstrated that a new schedule for the weekends in non-scholar periods could be considered due to its distinct profile from the remaining days. For future work, we propose to apply this methodology to larger data sets in time and in number, corresponding to different bus routes, in order to find a consensual cluster between all the routes. ©2010 IEEE.

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Abstract

Abstract
There is an increasing recognition that the competitive advantage of firms depends on their ability to create, transfer, utilize, develop and protect the Organisational knowledge assets, so it is logical to expect that more effective management of the creation and use of knowledge would accelerate a company's natural rate of learning, allow it to outpace competitors and create value for both customers and shareholders. The organisational learning can be seen as a combination of two phases, extraction and dissemination of management lessons, as we go from one project to another. The managers, executives and researchers in project management have to "learn how to learn", and maintain a consistent rate of improvement in learning processes. In a second phase of the organisational learning, it is necessary to learn how to code and disseminate knowledge for additional improvement of the organisation management. The research here reported is the first phase of a twofold objective it intends to identify the key elements that contribute to a learning environment and methodologies, processes and technologies that enhance the Organisational knowledge; and it intends to develop a corporative IT-tool, based on the conclusions and remarks obtained. It is our belief that the opinions gathered from people sensible to this topic, will allow us, among other things, to conclude some important aspects that will defiantly contribute for increasing the awareness given to knowledge acquisition and learning by the different team members.

Abstract
Today, organisations suffer a tremendous pressure to become more agile, efficient, profitable and dynamic. The market presents itself as an area of severe competition, where the margin between success and failure is becoming tenuous. Some authors have noticed the importance of meaningful and reliable information as a prerequisite of successful management and high-quality decision-making. Multiple factors, in the business environment were shown to cause impact in the organisational performance, such as the market attractiveness, the resources availability, clockspeed-industry, the environmental predictability, decision velocity and market opportunity discovery. The purpose of developing this research work was essentially to respond the following questions: Can the organisation improve performance by monitoring the market dynamics, by compiling information for the strategic positioning identification and market opportunities discovery? Can the information gathered agile the decision-making and the strategic alignment with the market? The market attractiveness index calculation phase intends to quantify the data collected from the multiple indicators, and then determine an index, for each market in the evaluation process. This technique is divided in three main steps, which consist of: (1) Calculating the mass density by category (Macro-Environment, Micro-Environment and Organisation), followed by (2) the product calculation of each category by the main market and, finally, (3) the prioritization of the different markets in study. The development of a methodology for assessing the market attractiveness is considered to be very relevant in the global market scenario. It can help organisations to gain a greater awareness of market dynamics, speed up the decision process and increase its consistency, thus enabling the strategic alignment and the improvement of the organisational performance.