A Context-Aware and Preference-Driven Vacation Planner for Tourism Regions

Taking a Preference SQL approach, a context-aware vacation planner for on-site activities is proposed to automatically generate vacation plans based on user preferences and situational aspects. Using different levels of abstraction, the result of the corresponding preference queries is always optimal and the result size is minimal. It consists of stereotype-specific and contextaware activities which are combined to create daily or even multi-day plans of activities. The correctness, completeness and optimality are assured by a preference calculus of strict partial orders. User preferences are initially collected and defined by a feedback questionnaire. The application is modelled by adequate preference compositions and the Preference SQL runtime system efficiently evaluates the resulting preference queries. The prototype proves that soft runtime requirements are met. Initial tests with real data from the industry-leading outdooractive platform indicate that the database-driven preference technology can successfully be employed to provide added value for vacation planning

Multidimensional clustering approaches for pareto-frontiers

In Data Mining large and increasing sets of data are becoming more and more common. In order to avoid losing the overview on these data-sets, preference queries are a very popular method to reduce quantities of data to high relevant information. Together with clustering methods like k-means, confusing sets of objects can be constituted and presented clearer in order to get a better overview. In this report we present on the one hand the Pareto-dominance as a very suitable and promising approach to cluster objects over better-than relationships. In order to meet someones desires, one can tip the balance of the final results to the more favored dimension if no decision for allocating objects is possible. On the other hand we introduce based on the Pareto-dominance an advanced clustering approach exploiting the Borda Social Choice voting rule to manage distances of different domains by equally weights during the clustering process