The present article introduces the outdoor activity tour suggestion problem (OATSP). This problem involves finding a closed path of maximal attractiveness in a transportation network graph, given a target path length and tolerance. Total path attractiveness is evaluated as the sum of the average arc attractiveness and the sum of the vertex prizes in the path. This problem definition takes its rise in the design of an interactive web application, which suggests closed paths for several outdoor activity routing modi, such as mountain biking. Both path length and starting point are specified by the user. The inclusion of POIs of some given types enrich the suggested outdoor activity experience. A fast method for the generation of heuristic solutions to the OATSP is presented. It is based on spatial filtering, the evaluation of triangles in a simplified search space and shortest path calculation. It generates valuable suggestions in the context of a web application. It is a promising method to generate candidate paths used by any local search algorithm, which further optimizes the solution.
Geographical information systems are commonly used for a variety of purposes. Many of them make use of a large database of geographical data, the correctness of which strongly influences the reliability of the system. In this paper, we present an approach to quality maintenance that is based on automatic discovery of non-perfect regularities in the data. The underlying idea is that exceptions to these regularities ('outliers') are considered probable errors in the data, to be investigated by a human expert. A case study shows how the tool can be used for extracting valuable knowledge about outliers in real-world geographical data, in an adaptive manner to the evolving data model supporting it. While the tool aims specifically at geographical information systems, the underlying approach is more broadly applicable for quality maintenance in data-rich intelligent systems.
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