HiveRel: hexagons visualization for relationship-based knowledge acquisition

The growing abundance in complex network data models is constantly increasing the challenges for non-expert users who perform an effective exploratory search in large data collections. In such domains, users search for entities related to a topic of interest and acquire knowledge by investigating the relationships between these entities. Designers, in turn, are challenged by the need to provide tools that enable convenient search and exploration to facilitate productive performance on the task. For this purpose, we introduce HiveRel, a search system that presents search results as tiled hexagons on a map-like surface with center-out relevance ordering and allows on-demand display of relationships between search results. HiveRel’s user interface is based on theoretical principles that reflect how users acquire knowledge through relationships. For the search mechanism, we provide a set of information retrieval definitions leading to the formalization of the Maximal n-Bounded Exploration Subgraph problem and present an implementation of a greedy heuristic algorithm that provides non-optimal solutions to this problem. We develop a proof of concept version of HiveRel. We evaluate it in two user studies that compare users’ performance using HiveRel to standard web search over a range of search knowledge acquisition tasks and two different domains. The results indicate that despite the lack of familiarity with the new system, users were generally more accurate and as fast using HiveRel, and provided positive evaluations for the search experience.