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The database community has long recognized the importance of graphical queryinterface to the usability of data management systems. Yet, relatively less hasbeen done. We present Orion, a visual interface for queryingultra-heterogeneous graphs. It iteratively assists users in query graphconstruction by making suggestions via machine learning methods. In its activemode, Orion automatically suggests top-k edges to be added to a query graph. Inits passive mode, the user adds a new edge manually, and Orion suggests aranked list of labels for the edge. Orion's edge ranking algorithm, RandomDecision Paths (RDP), makes use of a query log to rank candidate edges by howlikely they will match the user's query intent. Extensive user studies usingFreebase demonstrated that Orion users have a 70% success rate in constructingcomplex query graphs, a significant improvement over the 58% success rate bythe users of a baseline system that resembles existing visual query builders.Furthermore, using active mode only, the RDP algorithm was compared withseveral methods adapting other machine learning algorithms such as randomforests and naive Bayes classifier, as well as class association rules andrecommendation systems based on singular value decomposition. On average, RDPrequired 40 suggestions to correctly reach a target query graph (using only itsactive mode of suggestion) while other methods required 1.5--4 times as manysuggestions.

Orion: Enabling Suggestions in a Visual Query Builder for Ultra-Heterogeneous Graphs