Deep learning supported database systems (part 1)
In recent years, cardinality estimation in query optimization has been a popular area of research. With better estimation techniques, query optimizers can produce more efficient query plans that are able to directly impact the performance of Database Management Systems (DBMS). In this project, we w...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/158921 |
| _version_ | 1811683905622769664 |
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| author | Zhang, Yuhan |
| author2 | Gao Cong |
| author_facet | Gao Cong Zhang, Yuhan |
| author_sort | Zhang, Yuhan |
| collection | NTU |
| description | In recent years, cardinality estimation in query optimization has been a popular area of research. With better estimation techniques, query optimizers can produce more efficient query plans that are able to directly impact the performance of Database Management Systems (DBMS).
In this project, we will be extracting features from the text-based query plans of 5000 random queries on the IMDB dataset. Each of these query plans were generated by PostgreSQL, a widely used DBMS. Subsequently, we will map the extracted features to an RGB image format that can be fed as inputs into a shallow convolutional neural network (CNN). The model is tasked with a regression problem that aims to predict the actual execution time or rows returned by each query. Finally, the outputs of the model will be compared with the outputs generated by the PostgreSQL estimator. |
| first_indexed | 2024-10-01T04:20:10Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/158921 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:20:10Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1589212022-06-07T06:28:57Z Deep learning supported database systems (part 1) Zhang, Yuhan Gao Cong School of Computer Science and Engineering gaocong@ntu.edu.sg Engineering::Computer science and engineering::Information systems::Database management Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision In recent years, cardinality estimation in query optimization has been a popular area of research. With better estimation techniques, query optimizers can produce more efficient query plans that are able to directly impact the performance of Database Management Systems (DBMS). In this project, we will be extracting features from the text-based query plans of 5000 random queries on the IMDB dataset. Each of these query plans were generated by PostgreSQL, a widely used DBMS. Subsequently, we will map the extracted features to an RGB image format that can be fed as inputs into a shallow convolutional neural network (CNN). The model is tasked with a regression problem that aims to predict the actual execution time or rows returned by each query. Finally, the outputs of the model will be compared with the outputs generated by the PostgreSQL estimator. Bachelor of Engineering (Computer Science) 2022-06-07T06:28:57Z 2022-06-07T06:28:57Z 2022 Final Year Project (FYP) Zhang, Y. (2022). Deep learning supported database systems (part 1). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158921 https://hdl.handle.net/10356/158921 en SCSE20-0479 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Information systems::Database management Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Zhang, Yuhan Deep learning supported database systems (part 1) |
| title | Deep learning supported database systems (part 1) |
| title_full | Deep learning supported database systems (part 1) |
| title_fullStr | Deep learning supported database systems (part 1) |
| title_full_unstemmed | Deep learning supported database systems (part 1) |
| title_short | Deep learning supported database systems (part 1) |
| title_sort | deep learning supported database systems part 1 |
| topic | Engineering::Computer science and engineering::Information systems::Database management Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | https://hdl.handle.net/10356/158921 |
| work_keys_str_mv | AT zhangyuhan deeplearningsupporteddatabasesystemspart1 |