{"title":"XLJoins","authors":"A. Shanghooshabad","doi":"10.1145/3448016.3450582","DOIUrl":null,"url":null,"abstract":"Figure 1: An XLJoin example (QX from TPC-H benchmark): Structure learning component receives a join query, metadata, tables and existing models, and builds an MRF graph based on the query then while inferring the JAs (nodes showed in black), a BN is built, and finally, a uniform sample of JAs is generated using Ancestral sampling starting from the root to the leaves. Non-JAs (blue nodes) are added using the MRF once the JAs sampled from the BN because they do not affect uniformity.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"XLJoins\",\"authors\":\"A. Shanghooshabad\",\"doi\":\"10.1145/3448016.3450582\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Figure 1: An XLJoin example (QX from TPC-H benchmark): Structure learning component receives a join query, metadata, tables and existing models, and builds an MRF graph based on the query then while inferring the JAs (nodes showed in black), a BN is built, and finally, a uniform sample of JAs is generated using Ancestral sampling starting from the root to the leaves. Non-JAs (blue nodes) are added using the MRF once the JAs sampled from the BN because they do not affect uniformity.\",\"PeriodicalId\":87344,\"journal\":{\"name\":\"Proceedings. ACM-SIGMOD International Conference on Management of Data\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. ACM-SIGMOD International Conference on Management of Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3448016.3450582\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. ACM-SIGMOD International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3448016.3450582","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Figure 1: An XLJoin example (QX from TPC-H benchmark): Structure learning component receives a join query, metadata, tables and existing models, and builds an MRF graph based on the query then while inferring the JAs (nodes showed in black), a BN is built, and finally, a uniform sample of JAs is generated using Ancestral sampling starting from the root to the leaves. Non-JAs (blue nodes) are added using the MRF once the JAs sampled from the BN because they do not affect uniformity.
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