{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:19:21Z","timestamp":1750220361948,"version":"3.41.0"},"reference-count":3,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMOD Rec."],"published-print":{"date-parts":[[2021,6,15]]},"abstract":"<jats:p>One of the most important functionalities of a database system is to answer queries. We are interested in the following question: If there exists more than one answer to the given query, which one should the database report? There are two apparent choices: to return all the valid answers or to return one of them. The problem with the former choice is that it is often time-prohibitive to search for all valid answers. In the latter choice, fairness may become an issue, since the index built for fast search may introduce bias to the query result. For example, the index may favor a certain portion of the input data (e.g., nodes near the root of a tree index) and with a higher chance, output an answer related to that portion than other portions. Such bias can sometimes lead to undesirable consequences.<\/jats:p>","DOI":"10.1145\/3471485.3471495","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T05:22:06Z","timestamp":1623993726000},"page":"41-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Technical Perspective"],"prefix":"10.1145","volume":"50","author":[{"given":"Qin","family":"Zhang","sequence":"first","affiliation":[{"name":"Indiana University, Bloomington, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157469"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/276698.276876"}],"container-title":["ACM SIGMOD Record"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3471485.3471495","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3471485.3471495","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:26Z","timestamp":1750191446000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3471485.3471495"}},"subtitle":["Fair Near Neighbor Search via Sampling"],"short-title":[],"issued":{"date-parts":[[2021,6,15]]},"references-count":3,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,6,15]]}},"alternative-id":["10.1145\/3471485.3471495"],"URL":"https:\/\/doi.org\/10.1145\/3471485.3471495","relation":{},"ISSN":["0163-5808"],"issn-type":[{"type":"print","value":"0163-5808"}],"subject":[],"published":{"date-parts":[[2021,6,15]]},"assertion":[{"value":"2021-06-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}