ABSTRACT:One of the most important decisions a retailer can make is where to locate a retail outlet. Because convenience is so important to today's consumers, a retail store can prosper or fail solely based on its location. Recently, a changing retail environment is augmenting the location importance as retail economic groups develop multi outlet chains of small stores.The methods used in the development and calibration of location models for commercial spaces and sales forecast are multiple, varying from simple analogy forecast models to very complex spatial interactions models, which may incorporate dependence models in a gravitational or logit structure and many exploratory variables. More recent developments incorporating meta-heuristics such as genetic algorithms for the global problem of the multi outlet chain configuration, or the use of Voronoi diagrams in store trade area delimitation, are also presented. Finally, the Geographical Information Systems' role on the decision support process is equally explored.

In social network analysis, a k-clique is a relaxed clique, i.e., a kclique is a quasi-complete sub-graph. A k-clique in a graph is a sub-graph where the distance between any two vertices is no greater than k. The visualization of a small number of vertices can be easily performed in a graph. However, when the number of vertices and edges increases the visualization becomes incomprehensible. In this paper, we propose a new graph mining approach based on k-cliques. The concept of relaxed clique is extended to the whole graph, to achieve a general view, by covering the network with k-cliques. The sequence of k-clique covers is presented, combining small world concepts with community structure components. Computational results and examples are presented.

A paremiologic (study of proverbs) case is presented as part of a wider project based on data collected among the Azorean population. Given the considerable distance between the Azores islands, the authors present the hypothesis that there are significant differences in the proverbs from each island, thus permitting the identification of the native island of the interviewee based on his or her knowledge of proverbs. In this chapter, a feature selection algorithm that combines Rough Sets and the Logical Analysis of Data (LAD) is presented. The algorithm named LAID (Logical Analysis of Inconsistent Data) deals with noisy data, and the authors believe that an important link was established between the two different schools with similar approaches. The algorithm was applied to a real world dataset based on data collected using thousands of interviews of Azoreans, involving an initial set of twenty-two thousand Portuguese proverbs.

Recently, efforts have been made to add programming activities to the curriculum that promote computational thinking and foster 21st-century digital skills. One of the programming modalities is the use of Tangible Programming Languages (TPL), used in activities with 4+ year old children. In this review, we analyze solutions proposed for TPL in different contexts crossing them with non-TPL solutions, like Graphical Programming Languages (GPL). We start to characterize features of language interaction, their use, and what learning activities are associated with them. Then, in a diagram, we show a relation between the complexity of the languages with factors such as target age and output device types. We provide an analysis considering the type of input (e.g., TPL versus GPL) and output devices (e.g., physical robot versus graphical simulation) and evaluate their contribution to further insights about the general trends with respect to educational robotic systems. Finally, we discuss the opportunities to extend and improve TPLs based on the different solutions identified.

Abstract:This work is part of a supermarket chain expansion study and is intended to cluster the existent outlets in order to support the evaluation of outlet performance and new outlet site location. To overcome the curse of dimensionality (a large number of attributes for a very small number of existing outlets) experts' knowledge is considered in the clustering process. Three alternative approaches are compared for this end, the experts being required to: 1-a priori: provide values for perceived dissimilarities between pairs of outlets; 2-a posteriori: evaluate results from alternative regression trees; 3-interactively: help to select base variables and evaluate results from alternative dendrograms. The later approach provided the best results according to the marketing experts.