Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83505
Title: AutoG: a visual query autocompletion framework for graph databases
Authors: Xu, Jianliang
Yi, Peipei
Choi, Byron
Bhowmick, Sourav Saha
Keywords: Query autocompletion
Subgraph query
Issue Date: 2017
Source: Yi, P., Choi, B., Bhowmick, S. S., & Xu, J. (2017). AutoG: a visual query autocompletion framework for graph databases. The VLDB Journal, 26(3), 347-372.
Series/Report no.: The VLDB Journal
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.
URI: https://hdl.handle.net/10356/83505
http://hdl.handle.net/10220/42600
ISSN: 1066-8888
DOI: 10.1007/s00778-017-0454-9
Rights: © 2017 Springer-Verlag Berlin Heidelberg. This is the author created version of a work that has been peer reviewed and accepted for publication by The VLDB Journal, Springer-Verlag Berlin Heidelberg. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1007/s00778-017-0454-9].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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