{"title":"一种优化概念模型的方法","authors":"Ole Oren","doi":"10.1109/ICDE.1984.7271264","DOIUrl":null,"url":null,"abstract":"Optimization of a conceptual model is a non-trivial task. A set of rules for determining the \"best\" one out of a number of candidate solutions is introduced. A key point is the definition of a set of quantifiable characteristics of a conceptual model; a measurement of the characteristics together with a fixed measurement of the decision-maker's preferences are used to quantify the quality of the candidate solutions relative to each other. The chosen characteristics are: size, change pr. month, data description inaccuracy, semantic relevance, semantic inaccuracy and l/0-model size.","PeriodicalId":365511,"journal":{"name":"1984 IEEE First International Conference on Data Engineering","volume":"99 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1984-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A method for optimization of a conceptual model\",\"authors\":\"Ole Oren\",\"doi\":\"10.1109/ICDE.1984.7271264\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Optimization of a conceptual model is a non-trivial task. A set of rules for determining the \\\"best\\\" one out of a number of candidate solutions is introduced. A key point is the definition of a set of quantifiable characteristics of a conceptual model; a measurement of the characteristics together with a fixed measurement of the decision-maker's preferences are used to quantify the quality of the candidate solutions relative to each other. The chosen characteristics are: size, change pr. month, data description inaccuracy, semantic relevance, semantic inaccuracy and l/0-model size.\",\"PeriodicalId\":365511,\"journal\":{\"name\":\"1984 IEEE First International Conference on Data Engineering\",\"volume\":\"99 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1984-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1984 IEEE First International Conference on Data Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.1984.7271264\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1984 IEEE First International Conference on Data Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.1984.7271264","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimization of a conceptual model is a non-trivial task. A set of rules for determining the "best" one out of a number of candidate solutions is introduced. A key point is the definition of a set of quantifiable characteristics of a conceptual model; a measurement of the characteristics together with a fixed measurement of the decision-maker's preferences are used to quantify the quality of the candidate solutions relative to each other. The chosen characteristics are: size, change pr. month, data description inaccuracy, semantic relevance, semantic inaccuracy and l/0-model size.
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