In a previous paper, we laid out the vision of a novel graph query processing paradigm where instead of processing a visual query graph after its construction, it interleaves visual query formulation and processing by exploiting the latency offered by the GUI to filter irrelevant matches and prefetch partial query results [8]. Our first attempt at implementing this vision, called GBLENDER [8], shows significant improvement in system response time (SRT) for sub graph containment queries. However, GBLENDER suffers from two key drawbacks, namely inability to handle visual sub graph similarity queries and inefficient support for visual query modification, limiting its usage in practical environment. In this paper, we propose a novel algorithm called PRAGUE (Practical visu Al Graph QUery Blender), that addresses these limitations by exploiting a novel data structure called spindle-shaped graphs (SPIG). A SPIG succinctly records various information related to the set of super graphs of a newly added edge in the visual query fragment. Specifically, PRAGUE realizes a unified visual framework to support SPIG-based processing of modification-efficient sub graph containment and similarity queries. Extensive experiments on real-world and synthetic datasets demonstrate effectiveness of PRAGUE.

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 wider community. We present a novel HCI (human-computer interaction)-aware graph query processing paradigm, where instead of processing a query graph after its construction, it interleaves visual query construction and processing to improve system response time. We demonstrate a system called GBLENDER that exploits GUI latency to prune false results and prefetch candidate data graphs by employing a novel action-aware indexing scheme and a data structure called spindle-shaped graphs (SPIG). We demonstrate various innovative features of GBLENDER and its promising performance in evaluating subgraph containment and similarity queries.

Given a graph database D and a query graph g, an exact subgraph matching query asks for the set S of graphs in D that contain g as a subgraph. This type of queries find important applications in several domains such as bioinformatics and chemoinformatics, where users are generally not familiar with complex graph query languages. Consequently, user-friendly visual interfaces which support query graph construction can reduce the burden of data retrieval for these users. Existing techniques for subgraph matching queries built on top of such visual framework are designed to optimize the time required in retrieving the result set S from D, assuming that the whole query graph has been constructed. This leads to sub-optimal system response time as the query processing is initiated only after the user has finished drawing the query graph.