AutoG++: Towards Effective and Efficient Visual Graph Query Autocompletion (Byron Choi et al.)



Composing queries is evidently a tedious task. This is particularly true of graph queries as they are typically complex and prone to errors, compounded by the fact that graph schemas can be missing or too loose to be helpful for query formulation. Despite the great success of query formulation aids, in particular, automatic query completion, graph query autocompletion has received much less research attention.

In the past two years, Mr. Peipei YI, our PhD student, has developed a novel framework for subgraph query autocompletion (called AutoG). A prototype can be found at The process of autocompletion can be described as follows. Given an initial query q and a user's preference as input, AutoG returns ranked query suggestions Q' as output. Users may choose a query from Q' and iteratively apply AutoG to compose their queries. We have proposed ranking algorithms for computing effective query suggestions and indexing techniques for enhancing the efficiency of AutoG. Our existing results show that the query suggestions are useful (saved roughly *40%( of users' mouse clicks), and AutoG returns suggestions shortly under a large variety of parameter settings. The summer research explores new methodologies to improve the efficiency and effectiveness of subgraph query autocompletion.

For further information on this research topic, please contact Dr. Byron Choi.