Decomposition for a new kind of imprecise information system
Shaobo DENG, Sujie GUAN, Min LI, Lei WANG, Yuefei SUI
Decomposition for a new kind of imprecise information system
In this paper, we first propose a new kind of imprecise information system, in which there exist conjunctions (∧’s), disjunctions (∨’s) or negations (¬’s). Second, this paper discusses the relation that only contains ∧’s based on relational database theory, and gives the syntactic and semantic interpretation for ∧ and the definitions of decomposition and composition and so on. Then, we prove that there exists a kind of decomposition such that if a relation satisfies some property then it can be decomposed into a group of classical relations (relations do not contain ∧) that satisfy a set of functional dependencies and the original relation can be synthesized from this group of classical relations. Meanwhile, this paper proves the soundness theorem and the completeness theorem for this decomposition.Consequently, a relation containing ∧’s can be equivalently transformed into a group of classical relations that satisfy a set of functional dependencies. Finally, we give the definition that a relation containing ∧’s satisfies a set of functional dependencies. Therefore, we can introduce other classical relational database theories to discuss this kind of relation.
imprecise information systems / decomposition / composition / soundness and completeness
[1] |
Kantardzic M. Data Mining: Concepts, Models, Methods, and Algorithms. Hoboken, NJ: John Wiley & Sons, 2011
CrossRef
Google scholar
|
[2] |
Simovici D A, Tenney R L. Relational Database Systems. Orlando, FL: Academic Press, Inc., 1995
|
[3] |
Kryszkiewicz M. Rough set approach to incomplete information systems. Information Sciences, 1998, 112(1): 39–49
CrossRef
Google scholar
|
[4] |
Zadeh L A. Fuzzy sets. Information and Control, 1965, 8(3): 338–353
CrossRef
Google scholar
|
[5] |
Gau W L, Buehrer D J. Vague sets. IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(2): 610–614
CrossRef
Google scholar
|
[6] |
Buckles B P, Petry F E. A fuzzy representation of data for relational databases. Fuzzy Sets and Systems, 1982, 7(3): 213–226
CrossRef
Google scholar
|
[7] |
Ma Z M, Zhang F, Yan L, Cheng J W. Extracting knowledge from fuzzy relational databases with description logic. Integrated Computer-Aided Engineering, 2011, 18(2): 181–200
|
[8] |
Lu A, Ng W. Vague sets or intuitionistic fuzzy sets for handling vague data: Which one is better? In: Proceedings of International Conference on Conceptual Modeling. 2005, 401–416
CrossRef
Google scholar
|
[9] |
Zheng X M, Xu T, Ma Z F.A vague data model and induction dependencies between attributes. Journal of Nanjing University of Aeronautics & Astronautics, 2001, 33(4): 395–400
|
[10] |
Shen Q, Jiang Y L. Attribute reduction of multi-valued information system based on conditional information entropy. In: Proceedings of IEEE International Conference on Granular Computing. 2008, 562–565
|
[11] |
Wei W, Cui J B, Liang J Y, Wang J H. Fuzzy rough approximations for set-valued data. Information Sciences, 2016, 360(9): 181–201
CrossRef
Google scholar
|
[12] |
Zhong Y L. Attribute reduction of set-valued decision information system based on dominance relation. Journal of Interdisciplinary Mathematics, 2016, 19(3): 469–479
CrossRef
Google scholar
|
[13] |
Zhang Z Y, Yang X B. Tolerance-based multigranulation rough sets in incomplete systems. Frontiers of Computer Science, 2014, 8(5): 753–762
CrossRef
Google scholar
|
[14] |
Qiu T R, Liu Q, Huang H K. Granular computing based hierarchical concept capture algorithm in multi-valued information system. Pattern Recognition and Artifical Intelligence, 2009, 22(1): 22–27
|
[15] |
Motro A. Accommodating imprecision in database systems: issues and solutions. ACM SIGMOD Record, 1990, 19(4): 69–74
CrossRef
Google scholar
|
[16] |
Ben-Ari M. Mathematical Logic for Computer Science. 3rd ed. London: Springer-Verlag, 2012
CrossRef
Google scholar
|
/
〈 | 〉 |