Due to the complexity of graph query languages, the need for visual query interfaces that can reduce the burden of query formulation is fundamental to the spreading of graph data management tools to a wider community. Despite the significant progress towards building such query interfaces to simplify visual subgraph query formulation task, construction of current generation visual interfaces is not data-driven. That is, it does not exploit the underlying data graphs to automatically generate the contents of various panels in the interface. Such data-driven construction has several benefits such as superior support for subgraph query formulation and portability of the interface across different graph databases. In this demonstration, we present a novel data-driven visual subgraph query interface construction engine called DaVinci. Specifically, it automatically generates from the underlying database two key components of the visual interface to aid subgraph query formulation, namely canned patterns and node labels.

Presents a region-based matching algorithm, working on the color shading information (red, green and blue) contained in an image pair. In each color image, the regions are defined as connected areas within which the shading surface (i.e. the surface whose height at each location equals the color brightness in the image) maintains constant-sign Gaussian and mean curvatures. Region characteristics and adjacency information are explored by means of a region graph, in which each node corresponds to a particular region and is labeled with a vector of intraregional characteristics, while the arc joining two adjacent nodes is labeled with the relative position of the corresponding regions. The search for correspondences is restricted by means of a curvature consistency constraint, which states that matching regions must be embedded in areas of the shading surfaces having similar curvature characteristics. The disparity maps extracted from the R, G and B signals are combined at each resolution level of a coarse-to-fine strategy.>