SQL-ACT : content-based and history-aware input prediction for non-trivial SQL queries
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119534 |
| _version_ | 1826189010261770240 |
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| author | Manzi, Eric R |
| author2 | Samuel R. Madden. |
| author_facet | Samuel R. Madden. Manzi, Eric R |
| author_sort | Manzi, Eric R |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. |
| first_indexed | 2024-09-23T08:08:26Z |
| format | Thesis |
| id | mit-1721.1/119534 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:08:26Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1195342019-04-09T16:53:56Z SQL-ACT : content-based and history-aware input prediction for non-trivial SQL queries Content-based and history-aware input prediction for non-trivial SQL queries Manzi, Eric R Samuel R. Madden. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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). 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. by Eric R. Manzi. M. Eng. 2018-12-11T20:39:07Z 2018-12-11T20:39:07Z 2017 2017 Thesis http://hdl.handle.net/1721.1/119534 1066742369 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 81 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Manzi, Eric R SQL-ACT : content-based and history-aware input prediction for non-trivial SQL queries |
| title | SQL-ACT : content-based and history-aware input prediction for non-trivial SQL queries |
| title_full | SQL-ACT : content-based and history-aware input prediction for non-trivial SQL queries |
| title_fullStr | SQL-ACT : content-based and history-aware input prediction for non-trivial SQL queries |
| title_full_unstemmed | SQL-ACT : content-based and history-aware input prediction for non-trivial SQL queries |
| title_short | SQL-ACT : content-based and history-aware input prediction for non-trivial SQL queries |
| title_sort | sql act content based and history aware input prediction for non trivial sql queries |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119534 |
| work_keys_str_mv | AT manziericr sqlactcontentbasedandhistoryawareinputpredictionfornontrivialsqlqueries AT manziericr contentbasedandhistoryawareinputpredictionfornontrivialsqlqueries |