系統識別號 | U0002-1008200501091500 |
---|---|
DOI | 10.6846/TKU.2005.00147 |
論文名稱(中文) | 一種處理總括性分離資訊之擴充模糊關聯式資料庫 |
論文名稱(英文) | An Extended Fuzzy Relational Database with Inclusive-or Disjunctive Information |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 資訊工程學系博士班 |
系所名稱(英文) | Department of Computer Science and Information Engineering |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 93 |
學期 | 2 |
出版年 | 94 |
研究生(中文) | 王琮盛 |
研究生(英文) | Tsong-Sheng Wang |
學號 | 887190071 |
學位類別 | 博士 |
語言別 | 英文 |
第二語言別 | |
口試日期 | 2004-05-30 |
論文頁數 | 82頁 |
口試委員 |
指導教授
-
蔣定安
委員 - 黃俊堯 委員 - 蔣定安 委員 - 葛煥昭 委員 - 施國琛 委員 - 方鄒昭聰 |
關鍵字(中) |
分離資訊 擴充模糊關聯式資料庫 擴充模糊關聯式代數 多餘資訊 |
關鍵字(英) |
disjunctive information extended fuzzy relational database extended fuzzy relational algebra redundant information |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
在關聯式資料庫模式中會有不完整資訊的問題,此不完整資訊可分為無資訊,不確定資訊,分離式資訊及可能資訊,其中分離式資訊分為總括性分離資訊及互斥性分離資訊,總括性分離資訊的意思是分離資訊中至少有一個答案是對的,本論文主要探討關聯式資料庫中總括性分離式資訊。 首先我們提出一種擴充模糊關聯式模式來存放此資訊,其次為解決相關查詢問題,我們提出兩個參數來決定答案的明確性及不確定性,而此查詢的答案包括確定及可能的答案,並提出解決去除多餘資訊的方法。最後證明所提出的模式保留下傳統的關聯式模式的特性,這些特性包括資料庫惟一決定的表徵以及定義良好的關聯式代數運算。 |
英文摘要 |
Incomplete information in relational databases has been the subject of many studies. Based on these studies, incomplete information may fall into the following categories: null values, indefinite/disjunction information, and maybe information. Each disjunctive information can be explained as either inclusive-or or exclusive-or disjunctive information. Inclusive-or designates at least one answer in the case whereas exclusive-or designates one of answer in the case. This dissertation focuses entirely on the problem of inclusive-or disjunctive information in relational databases, and proposes an extended relational model to solve thed problem. This disseertaton proposes a logical reconstruction of the classical fuzzy relational database model to accommodate fuzzy disjunctive information. In query processing, we use two supplementary measurements, matching information and extra information, to model the concept of imprecision and uncertainty, respectively. We also take these two supplementary measures to determine the quality of answers to the query. The answers to the query thus contain sure answers and maybe answers. In addition, we discuss the redundancy problem; and present a complete set of fuzzy relational algebra with fuzzy disjunctive information. The proposed extended fuzzy relational database model preserves the properties of the classical fuzzy relational database model, including uniquely-determined and well-defined relational algebra |
第三語言摘要 | |
論文目次 |
Chapter 1 Introduction 4 1.1 Motivation 6 1.2 Research Objectives 9 1.3 Research Procedures 13 1.4 Organization of the Dissertation 15 Chapter 2 Review of the Related Literature 17 2.1 Conventional Relational Model 18 2.2 Classical Fuzzy Relational Database Model 20 Chapter 3 Measuring Qualities of Answers 23 3.1 Extended Fuzzy Relational Database 24 3.2 Integrity Constraints on the Extended Fuzzy Relational Database 31 3.3 Fuzzy Resemblance Relation 33 3.4 The Problem to be Solved 35 3.5 Answers to a Query 39 3.6 Matching Information 42 3.7 Extra Information 44 3.8 Qualities of Answers 47 Chapter 4 Redundant-Free Fuzzy Relations 50 4.1 Redundancy Problem 51 4.2 Redundancies Among Sub-tuples 52 4.3 Redundancies over An Extended Fuzzy Relation 54 4.4 Uniquely-Determined Relation 58 Chapter 5 Extended Fuzzy Relational Algebra 60 5.1 Extended Fuzzy Project and Select Operation 61 5.2 Extended Fuzzy Set Operations 66 Chapter 6 Conclusions 75 6.1 Contributions 75 6.2 Future Research 77 References 78 LIST OF TABLES Table 1. The EMP fuzzy relation ............................................................................... 6 Table 2. The EMPLOYEE fuzzy relation ................................................................ 10 Table 3. The EMP extended fuzzy relation.............................................................. 30 Table 4. The STUDENT fuzzy relation .................................................................... 36 Table 5. The answers of query ................................................................................. 36 Table 6. The extended fuzzy relation r .................................................................... 57 Table 7. The result of REDUCE(r) .......................................................................... 57 Table 8. The PERSON extended fuzzy relation....................................................... 63 Table 9.The EMPLOYEE extended fuzzy relation.................................................. 70 Table 10.The POSITION extended fuzzy relation................................................... 71 Table 11. Sure answers of the queries...................................................................... 72 |
參考文獻 |
P. Bosc and H. Prade, An introduction to the fuzzy set and possibility theory-based treatment of flexible queries and uncertain or imprecise databases. In. Uncertainty Management in Information Systems, A. Motro & P. Smets (eds), Kluwer Academic Publishers, (1997), 285-324. P. Bosc and O. Pivert, Towards an algebraic query language for possibilistic relations, 12th IEEE International Conf. on Fuzzy Systems (FUZZ-IEEE’2003), St. Louis, Missouri, USA B. P. Buckles and F. E. Petry, A fuzzy representation of data for relational databases, Fuzzy Sets and Systems 7(3), (1982), 213-226. B. P. Buckles and F. E. Petry, Information-theoretical characterization of fuzzy relational databases, IEEE Trans. on SMC. 13(1) ,(January/February 1983), 72-77. D. A. Chiang, Nancy P. Lin, and Chien-Chou Shis, Matching strengths of answers in fuzzy relational databases, IEEE Trans. on SMC. 28(3) (August 1998) 476-481. J.-S. Chiu and A.L.P. Chen,“An Exploration of relationships among exclusive disjunctive data,”IEEE Trans. on Know. and Data Eng., vol. 7, 6, pp. 928-940, Dec. 1995. J.-S. Chiu and A.L.P. Chen,“A Note on Incomplete relational database models based on intervals,”IEEE Trans. on Know. and Data Eng., vol. 8, 1, pp. 189-191, Feb. 1996. CODD, E. F. A relational model for large shared data banks. Commun. ACM 23, 6 (June 1970),377-387. E. F. Codd,“Extending the database relational model to capture more meaning,”ACM TODS, Vol. 4, 4, pp. 397-434, Dec. 1979. D. Dubois and H. Prade, The treatment of uncertainty in knowledge-based systems using fuzzy sets and possibility theory, International Journal of Intelligent Systems, 3, 2, (summer 1988), 141-165 GRANT, J., AND MINKER. J. Answering queries in indefinite databases and the null value problem. In Advances in Computing Research, P. Kanellakis, Ed., Vol. 3, The Theory of Databases; JAI Press, Inc., Greenwich, Ct. and London, 1986, pp. 247-267. N. C. Hsien, D. A. Chiang, Rick C. T. Chiang, Measuring the quality of queries in the fuzzy relational databases, International J. of Intelligent Systems, 16(2) (2001) 191-208. T. Imielinski and W. Lipski,“Incomplete information in relational databases,”JACM, vol. 31, no. 4, pp. 761-791. Sept. 1981. IMIELINSKI, T. On algebraic query processing in logical databases. In Aduances in Database Theory, Vol. 2, H. Gallaire, J. Minker, and J.-M. Nicolas, Eds. Plenum Press, New York and London, 1984, pp. 285-318. G. J. Klir and T. A. Folger, Fuzzy Sets, Uncertainty, and Information (Englewood Cliffs. 1988). J. Kacprzyk and A. Ziolkowski, Database queries with fuzzy linguistic quantifiers, IEEE Transactions on Systems, Man and Cybernetics, SMC-16, 3 (May/June 1986), 474-479 LIPSKI, W. On semantic issues connected with incomplete information systems. ACM Trans.Database Syst. 4, 3 (Sept. 1979), 262-296. W. Lipski,“On semantic issues connected with incomplete information databases,”ACM TODS, vol. 3, pp. 262-296. Sept. 1981 K. C. Liu and R. Sunderraman,“Indefinite and maybe information relational databases,”ACM TODS, vol. 15, No. 1, pp. 1-39, March. 1990. K.C. Liu and R. Sunderraman, On representating indefinite and maybe information in relational database: a generalization, Proc. Of the 6th IEEE Conf. on Data Engineering (1990) 495-502. K. C. Liu and R. Sunderraman, A generalized relational model for indefinite and maybe information, IEEE Transactions on Knowledge and Data Engineering, 3, 1, (March 1991), 65-77 De Luca and S. Termini. A definition of a non-probabilistic entropy in the setting of fuzzy set theory, Information and Control 20 (1972) 301-312. Z.M. Ma, F. Mili, Handling fuzzy information in extended possibility-based fuzzy relational databases, International Journal of Intelligent Systems, 17, 925-942, 2002. J. M. Medina, M. A. Vila, J. C. Cubero and O. Pons, Toward the implementation of a generalized fuzzy relational database model, Fuzzy Sets and System 75 (1995) 273-289. J. M. Morrissey, Imprecise information and uncertainty in information systems, ACM Trans. on Information System 8 (April 1990) 159-180. O. Pons, J. C. Cubero, J. M. Medina, and M. A. Vila, Dealing with disjunctive and missing information in logic fuzzy databases, Int. J. Uncertainty, Fuzziness and Knowledge-Based Systems 4(2), (1996) 177-201. H. Prade, Lipski’s approach to incomplete information database restated and generalized in the setting of Zadeh's possibility theory, Information System 9(1) (1984) 27-42. H. Prade and C. Testemale, Generalizing database relational algebra for the treatment of incomplete or uncertain information and vague queries, Information Sciences (34) (1984) 115-143. K. V. S. V. N. Raju and A. K. Majumdar, Fuzzy functional dependencies and lossless join decomposition on fuzzy relational database systems, ACM Trans. Database System 13 (1988) 129-166. R. Reiter, Towards a logical reconstruction of relational database theory, in: M. Brodie, J. Mylopoulos and J.W. Schmidt, Eds., On Conceptual Modeling (Springer, Berlin, 1984) 193-238. R. Reiter,“A sound and sometimes complete query evaluation algorithm for relational databases with null values,” JACM, vol . 33, no. 2, pp. 349-370, April, 1986. S. Shenoi and A. Melton, An extended version of the fuzzy relational database model, Information Science 52(1)(1990) 35-52. K.D. Supriya, B. Ranjit, and R.B. Akhil, On extended fuzzy relational database model with proximity relations, Fuzzy Sets and Systems, 117 (2001) 195-201,. M. Umano, FREEDOM-0: a fuzzy database system, In: M.M. Gupta and E. Sanchez, Eds., Fuzzy Information and Decision Processes (North-Holland, Amsterdam, 1982) 339-347. M. A. Vila, J. C. Cubero, J.M. Medina and O. Pons, On the use of logical definition of fuzzy relational databases, IEEE Conf. on Fuzzy Set (1993). M. A. Vila, J. C. Cubero, J.M. Medina and O. Pons, Uncertain Fuzzy Values still in the Framework of First Order Logic. International J. of Intelligent Systems, 17 (2002) 873–886. R. Yager, On the measure of fuzziness and negation part I: Membership in the unit interval, International J. General Systems 5 (1979) 221-229. J.D. Yang, Implicit Predicates for Handling Disjunctive Fuzzy Information in Fuzzy Databases, International J. of Intelligent Systems, 17 (2002) 1085-1100. A. Yazici, E.Gocmen, B.P.Buckles, R.George and F.E.Petry, An integrity constraint for a fuzzy relational databases, IEEE conference on fuzzy systems, 1993, pp. 496-499 L.A. Zadeh, Fuzzy sets, in J. Belzer, A.Holzman and A.Kenit, eds., Encyclopedia of Computer Science and Technology, vol. 8 (Mareel Dekker, New York, 1977), 325-351 L. A. Zadeh, Fuzzy sets as a basis for theory of possibility, Fuzzy Sets and Systems, 1(1) (1978) 3-28. M. Zemankova and A. Kandel, Implementing Imprecise in information Systems, Information Science 37 (1985) 107-141. |
論文全文使用權限 |
如有問題,歡迎洽詢!
圖書館數位資訊組 (02)2621-5656 轉 2487 或 來信