Query execution time estimation in graph databases based on graph neural networks
Query execution time estimation is an essential task for databases, accurate estimation results can help administrators to manage and monitor systems. This study proposes an interaction-aware and dependency-aware query execution time estimation approach that utilizes graph neural networks to capture...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157824001071 |
| _version_ | 1797234079821725696 |
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| author | Zhenzhen He Jiong Yu Tiquan Gu Dexian Yang |
| author_facet | Zhenzhen He Jiong Yu Tiquan Gu Dexian Yang |
| author_sort | Zhenzhen He |
| collection | DOAJ |
| description | Query execution time estimation is an essential task for databases, accurate estimation results can help administrators to manage and monitor systems. This study proposes an interaction-aware and dependency-aware query execution time estimation approach that utilizes graph neural networks to capture dependence and interaction relationships. We divide graph query execution time estimation tasks into three stages: workload generation and running, graph-based feature modeling and representation, training and estimation. Specifically, we generate query workloads and run them to collect the database and plan information when queries are executed. Then, the collected plan and database components are modeled into vertexes, the interaction and dependency between them are modeled into edges of graph-based feature representation. We develop an estimation model based on graph neural networks, in which the vertex embedding network is proposed to deal with the vertex heterogeneity, and the message passing network is proposed to aggregate the local representation into the global representation to obtain an embedding that can represent the higher-order feature information of the whole graph, and the estimation network is proposed to estimate execution times. The experiment results on datasets show that our estimation approach can improve estimation quality and outperform other estimation approaches in terms of estimation accuracy. |
| first_indexed | 2024-04-24T16:26:22Z |
| format | Article |
| id | doaj.art-f3107d8cf9364559bef5657a375da5d3 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-24T16:26:22Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-f3107d8cf9364559bef5657a375da5d32024-03-31T04:37:11ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782024-04-01364102018Query execution time estimation in graph databases based on graph neural networksZhenzhen He0Jiong Yu1Tiquan Gu2Dexian Yang3School of Information Science and Engineering, Xinjiang University, Urumqi 830049, China; Corresponding author.School of Information Science and Engineering, Xinjiang University, Urumqi 830049, China; School of Software, Xinjiang University, Urumqi 830046, ChinaSchool of Information Science and Engineering, Xinjiang University, Urumqi 830049, ChinaSchool of Information Science and Engineering, Xinjiang University, Urumqi 830049, ChinaQuery execution time estimation is an essential task for databases, accurate estimation results can help administrators to manage and monitor systems. This study proposes an interaction-aware and dependency-aware query execution time estimation approach that utilizes graph neural networks to capture dependence and interaction relationships. We divide graph query execution time estimation tasks into three stages: workload generation and running, graph-based feature modeling and representation, training and estimation. Specifically, we generate query workloads and run them to collect the database and plan information when queries are executed. Then, the collected plan and database components are modeled into vertexes, the interaction and dependency between them are modeled into edges of graph-based feature representation. We develop an estimation model based on graph neural networks, in which the vertex embedding network is proposed to deal with the vertex heterogeneity, and the message passing network is proposed to aggregate the local representation into the global representation to obtain an embedding that can represent the higher-order feature information of the whole graph, and the estimation network is proposed to estimate execution times. The experiment results on datasets show that our estimation approach can improve estimation quality and outperform other estimation approaches in terms of estimation accuracy.http://www.sciencedirect.com/science/article/pii/S1319157824001071Neo4j database management systemsDeep learningGraph neural networkGraph queriesExecution time estimation |
| spellingShingle | Zhenzhen He Jiong Yu Tiquan Gu Dexian Yang Query execution time estimation in graph databases based on graph neural networks Journal of King Saud University: Computer and Information Sciences Neo4j database management systems Deep learning Graph neural network Graph queries Execution time estimation |
| title | Query execution time estimation in graph databases based on graph neural networks |
| title_full | Query execution time estimation in graph databases based on graph neural networks |
| title_fullStr | Query execution time estimation in graph databases based on graph neural networks |
| title_full_unstemmed | Query execution time estimation in graph databases based on graph neural networks |
| title_short | Query execution time estimation in graph databases based on graph neural networks |
| title_sort | query execution time estimation in graph databases based on graph neural networks |
| topic | Neo4j database management systems Deep learning Graph neural network Graph queries Execution time estimation |
| url | http://www.sciencedirect.com/science/article/pii/S1319157824001071 |
| work_keys_str_mv | AT zhenzhenhe queryexecutiontimeestimationingraphdatabasesbasedongraphneuralnetworks AT jiongyu queryexecutiontimeestimationingraphdatabasesbasedongraphneuralnetworks AT tiquangu queryexecutiontimeestimationingraphdatabasesbasedongraphneuralnetworks AT dexianyang queryexecutiontimeestimationingraphdatabasesbasedongraphneuralnetworks |