SQL-ACT : content-based and history-aware input prediction for non-trivial SQL queries
Author(s)Manzi, Eric R
Content-based and history-aware input prediction for non-trivial SQL queries
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Samuel R. Madden.
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This thesis presents SqlAct, a SQL auto-completion system that uses content-based and history-aware input prediction to assist in the process of composing non-trivial queries. By offering the most relevant suggestions to complete the partially typed query first at the word-level and then at the statement-level, SqlAct hopes to help both novice and expert SQL developers to increase their productivity. Two approaches are explored: word-level suggestions are optimized based on the database's schema and content statistics, and statement-level suggestions that rely on Long Short-term Memory (LSTM) Recurrent Neural Networks language models trained on historical queries. The word-level model is integrated in a responsive command-line interface database client which is evaluated quantitatively and qualitatively. Results shows SqlAct provides a highly-responsive interface that makes high quality suggestions to complete the currently typed query. Possible directions for integration with the word-level model in the command-line tool are explored as well as the planned evaluation techniques.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 79-81).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.