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Autocompletion for Prefix-Abbreviated Input

CategoryPublications 226

Authors: Sheng Hu, Chuan Xiao, Jianbin Qin, Yoshiharu Ishikawa, Qiang Ma

Name of Conference: ACM Conference on Management of Data(SIGMOD 2019), June 30 – July 5, 2019, Amsterdam, The Netherlands

Date of Publication: June 30, 2019

Abstract

Query autocompletion (QAC) is an important interactive feature that assists users in formulating queries and saving keystrokes. Due to the convenience it brings to users, QAC has been adopted in many applications, including Web search engines, integrated development environments (IDEs), and mobile devices. For existing QAC methods, users have to manually type delimiters to separate keywords in their inputs. In this paper, we propose a novel QAC paradigm through which users may abbreviate keywords by prefixes and do not have to explicitly separate them. Such paradigm is useful for applications where it is inconvenient to specify delimiters, such as desktop search, text editors, and input method editors. E.g., in an IDE, users may input getnev and we suggest GetNextValue. We show that the query processing method for traditional QAC, which utilizes a trie index, is inefficient under the new problem setting. A novel indexing and query processing scheme is hence proposed to efficiently complete queries. To suggest meaningful results, we devise a ranking method based on a Gaussian mixture model, taking into consideration the way in which users abbreviate keywords, as opposed to the traditional ranking method that merely considers popularity. Efficient top-k query processing techniques are developed on top of the new index structure. Experiments demonstrate the effectiveness of the new QAC paradigm and the efficiency of the proposed query processing method.

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Keywords2019Big data analytics under constrained resourcesJianbin QinSIGMOD Previous: Next: