Interactive Graph Data Integration System With Spatial-Oriented Visualization and Feedback-Driven Provenance

This paper proposes new techniques in a visualization-enabled graph data quality assessment and alignment system leveraging feedback-driven provenance with goal of improving scalability, reducing execution time, and increasing relevance. The proposed system consists of following two components. First, graph data quality assessment with spatial-oriented visualization and feedback-driven provenance; in this section, we propose a new paradigm of feedback-driven provenance in order to streamline the collection of run-time provenance information based on user feedback with the goal of reducing execution time and improving relevance. We apply this idea in the context of graph data quality assessment and alignment, in which we propose a system that leverages user feedback on various components on the schema of a graph database during selection of samples of graphs to maintain provenance of representative nodes of samples. We utilize this provenance of representative nodes of samples to improve the effectiveness of future graph samples during quality assessment task. For the visualization component, we propose a solution based on the spatial-oriented approach with the goal of improving scalability, along with statistics and a visualization system based on the notion of heatmaps that involves utilizing quality information of dataset in order to assign to the spatial locations of various graph vertices on the screen varying degree of color intensity pixels. Second, graph data alignment leveraging spatial-oriented visualization and feedback-driven provenance: in this section, we propose a solution based feedback-driven provenance paradigm discussed earlier in context of graph databases by utilizing graph query logs as a feedback to select relevant data of neighborhoods of nodes that were matched during the process of graph alignment at run-time so as to improve its relevance and reduce execution time as well as a spatial-oriented solution to utilize the graph similarity measures in order to allocate spatial pixel positions to graph vertices as a part of a visual analytics tool with a focus on scalability allowing users to compare graphs visually.