臺灣博碩士論文加值系統

資料庫關聯表的正規化可用以避免資料重覆和更新異常，正規化強調不失真的切割表格，而切割的結果取決於值組的重覆性定義。在延伸可能性模糊資料模式中，值組間的相似度超過門檻值就被視為重覆值組，但因為值組間的相似度所構成之關係不具遞移性，所以無法根據相似度將值組分成數個不交集的群集，並合併同一群集中的值組。而在相似型模糊資料模式中，值組間相似度所構成的關係具有遞移性，故合併時不會發生前者的問題。本研究即探討利用現有的值組相似度計算方法及模糊功能相依之定義，找出在延伸可能性和相似型模糊資料下之關聯表的不失真切割。

Normalization of relations on relational databases can avoid the redundancies of data and update anomalies, and it emphasizes the lossless decomposition of relations. The outcome of decomposition depends on the definition of the redundancies of tuples. In Extended Possibility-Based fuzzy data model, if the resemblance between the tuples exceeds the threshold, we can say that they are redundant tuples, but the relation of the resemblance doesn’t have the transitivity, so that there is no way to divide the tuples into non overlapping groups, and to merge the tuples in the same group. But, in the Similarity-Based fuzzy data model, the resemblance relation of redundant tuples has transitivity, and when merging the tuples, there is no previous problem. This study just discussed how to use of the definitions of calculating the resemblance between tuples and the definitions of fuzzy functional dependencies up to now, and then find out a way to lossless decompose the relations in Extended- Possibility and Similarity-Based fuzzy data model.

中文摘要 …………………………………………………………………………….vi
英文摘要 ……………………………………………………………………………vii
誌謝…… ……………………………………………………………………………viii
目錄 ………………………………………………………………………………….ix
圖表目錄……………………………………………………………………………...xi
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的及重要性 2
1.4 論文架構 3
第二章 文獻探討 4
2.1 模糊資料模式 4
2.2屬性值相似度計算 6
2.3模糊功能相依 10
2.4模糊資料關聯表不失真切割 11
第三章 延伸可能性模糊資料模式關聯表之切割 14
3.1關聯表切割之方法與問題 14
3.2 PSE不失真之關聯表切割方式 17
3.3 PSE不失真之證明與範例 19
3.4 合併PSE重覆值組之演算法及限制 22
第四章 相似型模糊資料模式關聯表不失真之切割 24
4.1 相似型關聯表切割問題及方法 24
4.2 不失真切割證明與釋例 27
第五章 結論 30

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