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To the best of our knowledge, this is the first graph-embedding-based performance prediction model for concurrent queries. We first propose a graph model to encode query features, where each vertex is a node in the query plan of a query and each edge between two vertices denotes the correlations between them, e.g., sharing the same table\/index or competing resources. We then propose a prediction model, in which we use a graph embedding network to encode the graph features and adopt a prediction network to predict query performance using deep learning. Since workloads may dynamically change, we propose a graph update and compaction algorithm to adapt to workload changes. We have conducted extensive experiments on real-world datasets, and experimental results showed that our method outperformed the state-of-the-art approaches.<\/jats:p>","DOI":"10.14778\/3397230.3397238","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T11:46:24Z","timestamp":1593431184000},"page":"1416-1428","source":"Crossref","is-referenced-by-count":76,"title":["Query performance prediction for concurrent queries using graph embedding"],"prefix":"10.14778","volume":"13","author":[{"given":"Xuanhe","family":"Zhou","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Sun","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Feng","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,6,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/304182.304198"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.205"},{"key":"e_1_2_1_3_1","first-page":"167","volume-title":"CIDR","author":"Akdere M.","year":"2011","unstructured":"M. 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