This paper presents a technique for visualizing large spatial data sets in Web Mapping Systems (WMS). The technique creates a hierarchical clustering tree, which is subsequently used to extract clusters that can be displayed at a given scale without cluttering the map. Voronoi polygons are used as aggregation symbols to represent the clusters. This technique retains hierarchical relationships between data items at different scales. In addition, aggregation symbols do not overlap, and their sizes and the number of points that they cover is controlled by the same parameter. A prototype has been implemented and tested showing the effectiveness of the method for visualizing large data sets in WMS.

Search engines are powerful tools to find information on the Web. However, they commonly return many irrelevant documents when the users' queries are not specific enough. To refine the scope of their searches, refinement terms are sometimes recommended to the users by query expansion systems (QES). A recent wide-scale survey has shown that users seldom include these terms in their queries. In this article, we propose a new user-centric approach for evaluating QES. The purpose of our approach is to assess the suggestive power of the suggested terms. Several existing QES are compared following the proposed criteria.