{"title":"ATLaS系统及其强大的数据库语言基于SQL的简单扩展","authors":"Haixun Wang, C. Zaniolo","doi":"10.1109/ICDE.2002.994734","DOIUrl":null,"url":null,"abstract":"A lack of power and extensibility in their query languages has seriously limited the generality of DBMSs and hampered their ability to support new applications domains, such as data mining. In this paper, we solve this problem by stream-oriented aggregate functions and generalized table functions which are definable by users in the SQL language itself, rather than in an external programming language. These simple extensions turn SQL into a powerful database language, which can express a wide range of applications, including recursive queries, ROLAP (relational online analytical processing) aggregates, time-series queries, stream-oriented processing and data-mining functions. The SQL extensions are implemented in ATLaS (Aggregate and Table Language and System).","PeriodicalId":191529,"journal":{"name":"Proceedings 18th International Conference on Data Engineering","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The ATLaS system and its powerful database language based on simple extensions of SQL\",\"authors\":\"Haixun Wang, C. Zaniolo\",\"doi\":\"10.1109/ICDE.2002.994734\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A lack of power and extensibility in their query languages has seriously limited the generality of DBMSs and hampered their ability to support new applications domains, such as data mining. In this paper, we solve this problem by stream-oriented aggregate functions and generalized table functions which are definable by users in the SQL language itself, rather than in an external programming language. These simple extensions turn SQL into a powerful database language, which can express a wide range of applications, including recursive queries, ROLAP (relational online analytical processing) aggregates, time-series queries, stream-oriented processing and data-mining functions. The SQL extensions are implemented in ATLaS (Aggregate and Table Language and System).\",\"PeriodicalId\":191529,\"journal\":{\"name\":\"Proceedings 18th International Conference on Data Engineering\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 18th International Conference on Data Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.2002.994734\",\"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 18th International Conference on Data Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2002.994734","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The ATLaS system and its powerful database language based on simple extensions of SQL
A lack of power and extensibility in their query languages has seriously limited the generality of DBMSs and hampered their ability to support new applications domains, such as data mining. In this paper, we solve this problem by stream-oriented aggregate functions and generalized table functions which are definable by users in the SQL language itself, rather than in an external programming language. These simple extensions turn SQL into a powerful database language, which can express a wide range of applications, including recursive queries, ROLAP (relational online analytical processing) aggregates, time-series queries, stream-oriented processing and data-mining functions. The SQL extensions are implemented in ATLaS (Aggregate and Table Language and System).
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