{"title":"优化半结构化数据的索引","authors":"Liru Han, Xia-Qin Zheng, Xiao-Fang Li","doi":"10.1109/ICMLC.2002.1176762","DOIUrl":null,"url":null,"abstract":"We propose a set of strategies for optimizing the index for semistructured data. For example, for optimizing the path index, we propose the Improvea algorithm for mining association rules. Also, we propose a Colv algorithm. Based on these strategies, we provide optimizing algorithms for part of the basic query operations such as merger operation, selection operation and projection operation. These algorithms can improve query efficiency.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"10 1","pages":"303-306 vol.1"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing index for semistructured data\",\"authors\":\"Liru Han, Xia-Qin Zheng, Xiao-Fang Li\",\"doi\":\"10.1109/ICMLC.2002.1176762\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a set of strategies for optimizing the index for semistructured data. For example, for optimizing the path index, we propose the Improvea algorithm for mining association rules. Also, we propose a Colv algorithm. Based on these strategies, we provide optimizing algorithms for part of the basic query operations such as merger operation, selection operation and projection operation. These algorithms can improve query efficiency.\",\"PeriodicalId\":90702,\"journal\":{\"name\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"volume\":\"10 1\",\"pages\":\"303-306 vol.1\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2002.1176762\",\"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. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1176762","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We propose a set of strategies for optimizing the index for semistructured data. For example, for optimizing the path index, we propose the Improvea algorithm for mining association rules. Also, we propose a Colv algorithm. Based on these strategies, we provide optimizing algorithms for part of the basic query operations such as merger operation, selection operation and projection operation. These algorithms can improve query efficiency.
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