Interactive graph query language for multidimensional data in Collaboration Spotting visual analytics framework
This addresses the problem of improving human reasoning in visual analytics for users dealing with multidimensional data networks, though it appears incremental as it builds on existing frameworks.
The paper tackled the challenge of visual analytics for complex data networks by decomposing them into smaller sub-networks and developing an interactive graph query language for navigation, which enhances visual perception and exploration capabilities.
Human reasoning in visual analytics of data networks relies mainly on the quality of visual perception and the capability of interactively exploring the data from different facets. Visual quality strongly depends on networks' size and dimensional complexity while network exploration capability on the intuitiveness and expressiveness of user frontends. The approach taken in this paper aims at addressing the above by decomposing data networks into multiple networks of smaller dimensions and building an interactive graph query language that supports full navigation across the sub-networks. Within sub-networks of reduced dimensionality, structural abstraction and semantic techniques can then be used to enhance visual perception further.