This chapter focuses on the visualization of multi-domain search results. We start by positioning the problem in the recent line of evolution of search engine interfaces, which more and more are capable of mining semantic concepts and associations from text data and presenting them in sophisticated ways that depend on the type of the extracted data. The approach to visualization proposed in search computing extends current practices in several ways: the data to visualize are N-dimensional combinations of objects, with ranking criteria associated both to individual objects and to sets of combinations; object's properties can be classified in several types, for which optimized visualization families are preferred (e.g., timelines for temporal data, maps for geo-located information); combinations may exhibit any number of relevant properties to be displayed, which need to fit to the bi-dimensional presentation space, by emphasizing the most important attributes and de-emphasizing or hiding the less important ones. The visualization problem therefore amounts to deciding the best mapping between the data of the result set and the visualization space.@en

Search systems are becoming increasingly sophisticated in their capacity of building result sets that are not mere lists of documents but articulated combinations of concepts retrieved from different domains. This paper investigates the models for the result sets and the visualization spaces, and model-to-model transformations to dynamically suggest optimized visualizations for multi-domain search results.@en

Search systems are becoming increasingly sophisticated in their capacity of building results that are not mere lists of documents but articulated sets of concepts retrieved from different domains. As the search result sets exhibit more structure, techniques are required to visualize the retrieved objects in a way that facilitates the immediate understanding of their properties and relationships. This paper investigates the use of models to represent both result sets and visualization spaces, and of model-to-model transformations to dynamically suggest an optimized result visualization for multi-domain search.@en