模糊語意測度(semantic measure)是比較兩個模糊集(fuzzy sets)的相似程度;並且,當模糊集運用於關聯式資料庫(Relational Database:RDB),即模糊關聯式資料庫(Fuzzy Relational Database:FRDB),則模糊功能相依性(fuzzy functional dependency)是達成資料正規化(normalize)的主要依據,為的是減少資料重複(data redundancy)與更新異常(update anormal),並提高資料庫執行效能與降低成本。 本論文修正趨近相等(approximately equal)的定義,並解決模糊集運算Alpha composition,在數、向量與矩陣運算不一致(not consistent)的問題;進而依據幾何距離模型為基礎的量測法(measure based on the geometric distance model),提出新的語意測度(semantic measure),以及相對應的模糊功能相依性(fuzzy functional dependency)定義;使得本研究所提出之語意測度,不僅可以運用於模糊值與模糊集的相似量測,拓展模糊理論(fuzzy theory)的應用空間,更可將模糊值與模糊集運用於模糊關聯式資料庫,使資料可以完整有效地管理。
Semantic measure is a method to compare the similarity degrees of two fuzzy sets. When using the fuzzy sets in relational database(RDB), called fuzzy relational database(FRDB), fuzzy functional dependency is a way to normalize relational table, and eliminate the redundancy of data and avoid update anormal. So that we can make the efficiency of database higher and reduce the maintaining cost. In this thesis, we provide a new definition of approximately equal and resolve the inconsistence of Alpha composition among operating fuzzy values, fuzzy vectors and fuzzy matrixes. Furthermore, according to geometric distance measured base, we propose a new model of semantic measure and related definition of fuzzy functional dependency. The new semantic measure can be applied to not only the semantic measure of fuzzy values and fuzzy sets, but also the fuzzy relational database. So the fuzzy data can be managed completely and effectively.
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