# AutoG: a visual query autocompletion framework for graph databases

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## Abstract

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 this paper, we propose a novel framework for subgraph query autocompletion (called AutoG). 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. The novelties of AutoG are as follows: First, we formalize query composition. Second, we propose to increment a query with the logical units called *c*-*prime features* that are (i) frequent subgraphs and (ii) constructed from smaller *c*-prime features in no more than *c* ways. Third, we propose algorithms to rank candidate suggestions. Fourth, we propose a novel index called *feature * Dag (FDag) to optimize the ranking. We study the query suggestion quality with simulations and real users and conduct an extensive performance evaluation. The 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.

## Keywords

Subgraph query Query autocompletion Graphs Database usability## Notes

### Acknowledgements

Peipei Yi and Byron Choi are partially supported by the HK-RGC GRF 12201315 and 12232716. Sourav S Bhowmick is supported by the Singapore MOE AcRF Tier-1 Grant RG24/12 and MOE AcRF Tier-2 Grant 2015-T2-1-040. Jianliang Xu is partially supported by the HK-RGC GRF 12244916 and 12200114.

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