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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2018, Vol. 12 Issue (2) : 376-395    https://doi.org/10.1007/s11704-017-4436-2
RESEARCH ARTICLE
Decomposition for a new kind of imprecise information system
Shaobo DENG1, Sujie GUAN1, Min LI1(), Lei WANG, Yuefei SUI2()
1. School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
2. Key Laboratory of Intelligent Information, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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Abstract

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.

Keywords imprecise information systems      decomposition      composition      soundness and completeness     
Corresponding Author(s): Min LI,Yuefei SUI   
Just Accepted Date: 21 February 2017   Online First Date: 07 September 2017    Issue Date: 23 March 2018
 Cite this article:   
Shaobo DENG,Sujie GUAN,Min LI, et al. Decomposition for a new kind of imprecise information system[J]. Front. Comput. Sci., 2018, 12(2): 376-395.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-017-4436-2
https://academic.hep.com.cn/fcs/EN/Y2018/V12/I2/376
1 Kantardzic M. Data Mining: Concepts, Models, Methods, and Algorithms. Hoboken, NJ: John Wiley & Sons, 2011
https://doi.org/10.1002/9781118029145
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
https://doi.org/10.1016/S0020-0255(98)10019-1
4 Zadeh L A. Fuzzy sets. Information and Control, 1965, 8(3): 338–353
https://doi.org/10.1016/S0019-9958(65)90241-X
5 Gau W L, Buehrer D J. Vague sets. IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(2): 610–614
https://doi.org/10.1109/21.229476
6 Buckles B P, Petry F E. A fuzzy representation of data for relational databases. Fuzzy Sets and Systems, 1982, 7(3): 213–226
https://doi.org/10.1016/0165-0114(82)90052-5
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
https://doi.org/10.1007/11568322_26
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
https://doi.org/10.1016/j.ins.2016.04.005
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
https://doi.org/10.1080/09720502.2015.1047610
13 Zhang Z Y, Yang X B. Tolerance-based multigranulation rough sets in incomplete systems. Frontiers of Computer Science, 2014, 8(5): 753–762
https://doi.org/10.1007/s11704-014-3141-7
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
https://doi.org/10.1145/122058.122066
16 Ben-Ari M. Mathematical Logic for Computer Science. 3rd ed. London: Springer-Verlag, 2012
https://doi.org/10.1007/978-1-4471-4129-7
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