AutoG: a visual query autocompletion framework for graph databases

Peipei Yi, Byron Koon Kau Choi, Sourav S. Bhowmick, Jianliang Xu

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)


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 demonstration, we present a novel interactive visual subgraph query autocompletion framework called AUTOG which alleviates the potentially painstaking task of graph query formulation. Specifically, given a large collection of small or medium-sized graphs and a visual query fragment q formulated by a user, AUTOG returns top-k query suggestions Q' as output at interactive time. Users may choose a query from Q' and iteratively apply AUTOG to compose their queries. We demonstrate various features of AUTOG and its superior ability to generate high quality suggestions to aid visual subgraph query formulation.

Original languageEnglish
Pages (from-to)1505-1508
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number13
Publication statusPublished - 2015
Event42nd International Conference on Very Large Data Bases, VLDB 2016 - New Delhi, India
Duration: 5 Sept 20169 Sept 2016

Scopus Subject Areas

  • Computer Science (miscellaneous)
  • Computer Science(all)


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