{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T19:39:23Z","timestamp":1771616363515,"version":"3.50.1"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>Performance of worst-case optimal join algorithms depends on the order in which the join attributes are processed. It is challenging to identify suitable orders prior to query execution due to the huge search space of possible orders and unreliable execution cost estimates in case of data skew or data correlation.<\/jats:p>\n          <jats:p>We demonstrate ADOPT, a novel query engine that integrates adaptive query processing with a worst-case optimal join algorithm. ADOPT divides query execution into episodes, during which different attribute orders are invoked. With runtime feedback on performance of different attribute orders, ADOPT rapidly approaches near-optimal orders. Moreover, ADOPT uses a unique data structure which keeps track of the processed input data to prevent redundant work across different episodes. It selects attribute orders to try via reinforcement learning, balancing the need for exploring new orders with the desire to exploit promising orders. In experiments, ADOPT outperforms baselines, including commercial and open-source systems utilizing worst-case optimal join algorithms, particularly for complex queries that are difficult to optimize.<\/jats:p>","DOI":"10.14778\/3611540.3611629","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:32:37Z","timestamp":1694777557000},"page":"4094-4097","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning"],"prefix":"10.14778","volume":"16","author":[{"given":"Junxiong","family":"Wang","sequence":"first","affiliation":[{"name":"Cornell University, Ithaca, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitchell","family":"Gray","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Immanuel","family":"Trummer","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmet","family":"Kara","sequence":"additional","affiliation":[{"name":"University of Zurich, Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Olteanu","sequence":"additional","affiliation":[{"name":"University of Zurich, Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","unstructured":"Christopher R. Aberger Susan Tu Kunle Olukotun and Christopher R\u00e9. 2016. EmptyHeaded: a relational engine for graph processing. In SIGMOD. 431--446. arXiv:1503.02368 10.1145\/2882903.2915213","DOI":"10.1145\/2882903.2915213"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2742796"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409360.1409380"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/11871842_29"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-017-0480-7"},{"key":"e_1_2_1_6_1","unstructured":"Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http:\/\/snap.stanford.edu\/data."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/16856.16888"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3464389"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5441\/002\/icdt.2014.13"},{"key":"e_1_2_1_10_1","volume-title":"ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning. arXiv preprint arXiv:2307.16540","author":"Wang Junxiong","year":"2023","unstructured":"Junxiong Wang, Immanuel Trummer, Ahmet Kara, and Dan Olteanu. 2023. ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning. arXiv preprint arXiv:2307.16540 (2023)."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3611540.3611629","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:35:43Z","timestamp":1757543743000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3611540.3611629"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":10,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10.14778\/3611540.3611629"],"URL":"https:\/\/doi.org\/10.14778\/3611540.3611629","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"2023-08-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}