臺灣博碩士論文加值系統

(44.220.247.152) 您好！臺灣時間：2024/09/15 08:28

:::

詳目顯示

:

• 被引用:0
• 點閱:448
• 評分:
• 下載:8
• 書目收藏:1
 本研究之目的在於探討如何有效率的發掘動態關聯式資料庫及動態模糊關聯式資料庫中之功能相依性(functional dependency; FD)。功能相依是描述資料庫關聯中屬性之間的一種函數關係。從關聯式資料庫中發掘功能相依性可以應用於資料庫設計、查詢最佳化及資料庫安全上。因此功能相依性之探勘技術已被視為一項重要的資料庫分析工具，最近幾年更受到相當多的重視。然而多數的研究著重在靜態的資料庫上發掘功能相依性，只有少數研究考慮到動態資料庫之探勘技術。若能進一步改善動態資料庫功能相依性之探勘技術，則可使其更具實用價值。 因此我們針對一般之關聯式資料庫及以類似關係為主之模糊關聯式資料庫，在資料庫有所變動時（新增資料），提出三個有效率的功能相依性之漸增式探勘方法。第一個方法是在一般之關聯式資料庫上,提出一個以分割(partition)為主之功能相依性漸增式探勘演算法。第二個方法是在以類似關係為主之模糊關聯式資料庫上，提出一個以逐對比較(pair-wise comparison)之功能相依性漸增式探勘演算法。第三個方法亦是在以類似關係為主之模糊關聯式資料庫上，提出一個以分割為主之功能相依性漸增式探勘演算法。同時我們並對三個方法之計算複雜度提出分析比較及實驗。結果顯示，三個方法皆能相當有效地找出一組最小的功能相依性組合。
 The goal of this research is to study how to improve the incremental discoveries of functional dependencies (FD) from crisp and fuzzy relational databases. Functional dependencies are relationship between attributes of a database relation. Discovery of FD’s from relational databases can be applied to database design, database reverse engineering, query optimization, and database security. It has been identified as an important database analysis technique and received considerable research interests in recent years. However, most studies emphasize on non-incremental searching techniques of FD’s from static databases. Few have considered the incremental searching techniques that are required when the database is updated incrementally. More researches are needed to improve current incremental searching techniques to deal with dynamic database behaviors in the real world. In this research, we proposed three efficient incremental searching algorithms of functional dependencies for crisp relational databases and similarity-based fuzzy relational databases, when a set of tuples is added to the database. Algorithm I is a partition-based incremental searching algorithm on crisp relational database. Algorithm II is a pairwise-comparison-based incremental searching algorithm on similarity-based fuzzy relational database. Algorithm III is a partition-based incremental searching algorithm on similarity-based fuzzy relational database. We also present the complexity analysis and experimental results of these three algorithms. The results show that the proposed algorithms could find the minimal cover of functional dependencies efficiently.
 摘要 II ABSTRACT III ACKNOWLEDGEMENTS V LIST OF FIGURES VIII LIST OF TABLES IX CHAPTER 1 INTRODUCTION 1 1.1 Background 1 1.2 Motivation 3 1.3 Thesis Organization 4 CHAPTER 2 LITERATURE SURVEY 5 2.1 Discovery of Functional Dependencies from Relational Databases 5 2.2 Incremental Discovery of FD’s from Relational Databases 8 2.3 The Similarity-based Fuzzy Data Model 10 2.3.1 Similarity Relation and Domain Partitions 10 2.3.2 The Similarity-based Fuzzy Relational Data Model 12 2.3.3 Functional Dependencies on the Similarity-based Data Model 14 CHAPTER 3 INCREMENTAL SEARCHING ALGORITHM OF FD’s FROM RELATIONAL DATABASES 19 3.1 Notation 19 3.2 Algorithm I 20 3.3 Example 23 CHAPTER 4 INCREMENTAL SEARCHING ALGORITHMS OF FD’s FROM SIMILARITY-BASED FUZZY RELATIONAL DATABASES 28 4.1 Validation of FD’s by Fuzzy Relational Operations 28 4.2 Monotonicity of FD’s 31 4.3 Notation 33 4.4 Algorithm II 34 4.5 Algorithm III 36 4.6 Example 41 CHAPTER 5 ANALYSIS 45 CHAPTER 6 EXPERIMENTS 49 CHAPTER 7 CONCLUSION AND FUTURE WORK 54 REFERENCES 55 PUBLICATION LIST 59
 [1] M. Anvari, and G.F. Rose, “Fuzzy Relational Databases,” in Bezdek, Ed., Analysis of Fuzzy Information, Vol. II (CRC Press, Boca Raton, FL, 1987).[2] S. Bell and P. Brockhausen, “Discovery of Data Dependencies in Relational Databases,” Tech. Rep. LS-8, Report-14, University of Dortmund, Apr. 1995.[3] P. Bosc, and O. Pivert, “Fuzzy Querying in Conventional Databases,” In Fuzzy Logic for the Management of Uncertainty, Zadeh, L. and Kacprzyk, J. Eds, John Wiley, New York, 1992, 645-671.[4] P. Bosc, L. Lietard and O. Pivert, “Functional Dependencies Revisited Under Graduality and Imprecision,” NAFIPS, 1997, 57-62.[5] B.P. Buckles and F.E. Petry, “A Fuzzy Representation of Data for Relational Databases,” Fuzzy Sets and Systems, 7, 1982, 213-226.[6] G.Q. Chen, “Fuzzy Functional Dependencies and A Series of Design Issues of Fuzzy Relational Databases,” in Fuzziness in Database Management Systems, P. Bosc, J. Kacprzyk, eds, Physica Verlag, 1995, 166-185.[7] G.Q. Chen, Fuzzy Logic in Data Modeling: Semantics, Constraints, and Database Design, Kluwer Academic Publishers, 1998.[8] P.A. Flach, “Inductive Characterisation of Database Relations,” TIK Research Report, November, 1990.[9] P.A. Flach and I. Savnik, “Database Dependency Discovery: A Machine Learning Approach,” AI Communications, 12(3), November 1999, 139-160[10] R. Haux and U. Eckert, “Nondeterministic Dependencies in Relations: An Extension of The Concept of Functional Dependency,” Information Systems, 10 (2), 1985, 139-148.[11] Y. Huhtala, J Karkkainen, P. Porkka, and H. Toivonen, “Efficient Discovery of Functional and Approximate Dependencies Using Partitions,” Proceedings of IEEE International Conference on Data Engineering, 1998, 392-410.[12] M. Maddouri, S. Elloumi, and A. Jaoua, “An Incremental Learning System for Imprecise and Uncertain Knowledge Discovery,” Journal of Information Sciences, 1998, 109, 149-164.[13] H. Mannila and K. J. Räihä, “Algorithms for Inferring Functional Dependencies,” Data & Knowledge Engineering, 12(1), 1994, 83-99.[14] H. Mannila and H. Toivonen, “Levelwise Search and Borders of Theories in Knowledge Discovery,” Data Mining and Knowledge Discovery, 1 (3), 1997, 241-258.[15] J.M. Medina, M.A. Vila, J.C. Cubero, and O. Pons, “Towards the Implementation of A Generalized Fuzzy Relational Database Model,” Fuzzy Sets and Systems, 75, 1995, 273-289.[16] S. Parthasarathy, M. J. Zaki, M. Ogihara, and S. Dwarkadas, “Incremental and Interactive Sequence Mining,” ACM CIKM’99, 1999, 251-258.[17] K.V.S.V.N. Raju and A.K. Majumdar, “Fuzzy Functional Dependencies and Lossless Join Decomposition of Fuzzy Relational Database Systems,” ACM Transactions on Database Systems, 13 (2), 1988, 129-166.[18] H. Sachar, “Theoretical Aspects of Design of and Retrieval from Similarity-based Relational Database Systems,” Ph.D. Diss., 1986, Univ. of Texas at Arlington, TX, USA.[19] I. Savnik, and P. Flach, “Bottom-up Induction of Functional Dependencies from Relations,” In Knowledge Discovery in Databases Workshop, G. Piatetsky-Shapiro, Editor, Paper from the 1993 AAAI Workshop, 174-185.[20] A.N. Saharia, and T.M. Barron, “Approximate Dependencies in Database Systems,” Decision Support Systems, 13, 1995, 335-347.[21] J.C. Schlimmer, “Efficiently Inducting Determinations: A Complete and Systematic Search Algorithm that Using Optimal Pruning,” In G. Piatetsky-Shapiro, Editor, Proceedings of the Tenth International Conference on Machine Learning, Morgan Kaufmann, 1993, 284-290.[22] S. Shenoi, A. Melton, and L.T. Fan, “An Equivalence Class Model of Fuzzy Relational Databases,” Fuzzy Sets and Systems, 38, 1990, 153-170.[23] S. Shenoi, and A. Melton, “Proximity Relations in The Fuzzy Relational Database Model,” Fuzzy Sets and System, 31, 1989, 285-296.[24] Ning Shan, and Wojciech Ziarko, “Data-Based Acquisition and Incremental Modification of Classification Rules,” Computational Intelligence, Vol. 11, 2, 1995, 357-370.[25] Jian Tang, “Using Incremental Pruning to Increase the Efficiency of Dynamic Itemset Counting for Mining Association Rules,” ACM 7th International Conference on Information and Knowledge Management, 1998, 273-280.[26] M. Umano, “Freedom-O: A Fuzzy Database System, in Gupta-Sanchez,” Ed., Fuzzy Information and Decision Processes (North-Holland, Amsterdam, 1982).[27] S.L. Wang, J.S. Tsai, and T.P. Hong, “Discovering Functional Dependencies from Similarity-based Fuzzy Relational Databases,” To Appear in Journal of Intelligent Data Analysis.[28] A. Yazici, E. Gocmen, B.P. Buckles, and R. George, and F.E. Petry, “An Integrity Constraint for A Fuzzy Relational Database,” Proc. of Second IEEE Int. Conf. on Fuzzy Systems 1993, Vol: 1, 496-499.[29] L.A. Zadeh, “Similarity Relations and Fuzzy Orderings,” Info. Sci., vol 3, no. 1, Mar. 1971, 177-200.[30] M. Zemankova-Leech and A. Kandel, Fuzzy Relational Databases - A Key to Expert Systems, Verlag TUV Rheinland, Cologne, 1985.
 電子全文
 國圖紙本論文
 推文當script無法執行時可按︰推文 網路書籤當script無法執行時可按︰網路書籤 推薦當script無法執行時可按︰推薦 評分當script無法執行時可按︰評分 引用網址當script無法執行時可按︰引用網址 轉寄當script無法執行時可按︰轉寄

 1 模糊關聯式資料庫近似相依性之探勘 2 功能相依性探勘之維護 3 資料庫反向工程中資料相依性之探勘

 1 23.吳志傑,”溫度感測元件及其市場”,材料與社會叢書1,P.27~31 2 9.吳仁傑,”納米複合材料聚合技術”,工業材料125期,86年5月,P.115~119 3 8.蔡宗燕,”納米級無機層材之開發與應用”,工業材料125期,86年5月,P.120~128 4 6.郭文法,”納米複合材料加工應用”,工業材料125期,86年5月,P.129~135 5 1. 廖建勛,”納米高分子複合材料”,工業材料125期,86年5月,P.108~114

 1 資料庫反向工程中資料相依性之探勘 2 組織文化、標竿選擇要項對企業標竿學習傾向之影響---以一般製造業、科技性製造業與服務業為對象之比較研究 3 增進模擬法估計風險值績效之研究－以台灣股票市場為例 4 模糊多準則決策方法之應用---以國軍軍官考績評鑑為例 5 脂肪肝預測模式之研究 6 高科技產業從業人員休閒行為、工作壓力與工作績效之研究 7 學校組織衝突、抗拒、環境和個人特質對校長角色壓力之探討研究 8 推行學習型組織阻力因素之探索式研究-以學習歷史為方法 9 安養單位長期照護者之人格類型與工作適配之研究 10 以輸入資訊內涵觀點構建台灣股價指數類神經網路預測模式之研究 11 跨國企業移轉計價與國際租稅策略之研究 12 衰變率服從韋伯分配之衰變試驗的最佳設計 13 電子商務交易課税問題之研究 14 無本金交割遠期外匯與即期外匯市場之相關性分析 15 海關政風單位角色之研究與執行成效分析

 簡易查詢 | 進階查詢 | 熱門排行 | 我的研究室