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A statistical approach to incomplete information in database systems
ACM Transactions on Database Systems, 1982There are numerous situations in which a database cannot provide a precise answer to some of the questions that are posed. Sources of imprecision vary and include examples such as recording errors, incompatible scaling, and obsolete data. In many such situations, considerable prior information concerning the imprecision exists and can be exploited to ...
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Formal concept analysis of incomplete information system
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010Both concepts of information system and many-valued context are introduced and the incomplete information system (IIS) is processed. By adopting formal concept analysis, the many-valued context of the processed IIS is transformed into a one-valued one by adopting compound conceptual scaling in a many-valued context.
Jun Liu, Xiao-qiu Yao
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Generalized rules in incomplete information systems
1997In the paper we define a notion of a generalized decision rule in a system with incomplete information. A generalized rule may be indefinite. A definite generalized rule is called certain. A rule is defined as generalized in an incomplete system if it is generalized in every completion of the incomplete system.
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Admission Control with Incomplete Information of a Queueing System
Operations Research, 2003We consider a multiple-server loss model where customers arrive at a gatekeeper according to a Poisson process. A costcis incurred if a new arrival is blocked from entering the system by the gatekeeper, while a larger cost K is incurred if an admitted customer finds all servers busy and therefore has to leave the system. The key assumption is that the
Kyle Y. Lin, Sheldon M. Ross
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Interval Granules in Incomplete Information Systems
Advanced Materials Research, 2011Rough set theory is an efficient mathematical theory for data reduction and knowledge discovery of various fields. However, classical rough set theory is not applicable for knowledge induction of incomplete information systems. In this paper, a concept of interval granule is presented.
Xiao Hui Chen, Ren Pu Li, Zhi Wang Zhang
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Entropies and Co–entropies for Incomplete Information Systems
2007A partitioning approach to the problem of dealing with the entropy of incomplete information systems is explored. The aim is to keep into account the incompleteness and at the same time to obtain a probabilistic partition of the information system. For the resulting probabilistic partition, measures of entropy and co-entropy are defined, similarly to ...
Daniela Bianucci +2 more
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Optimal Control of Uncertain Systems with Incomplete Information for the Disturbances
SIAM Journal on Control and Optimization, 2004Summary: We investigate the problem of optimization of a terminal cost function for a system depending on a control, and on two disturbances for which a priori set membership is known. The disturbances are of different natures: One becomes known to the controller at the current time (we called it observable) while the other remains unknown.
Marc Quincampoix, Vladimir M. Veliov
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A NEW METHOD FOR MEASURING THE UNCERTAINTY IN INCOMPLETE INFORMATION SYSTEMS
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2009Based on an intuitionistic knowledge content nature of information gain, the concept of combination entropy CE(A) in incomplete information systems is first introduced, and some of its important properties are given. Then, the conditional combination entropy CE(Q | P) and the mutual information CE(P;Q) are defined. Unlike all existing measures for the
Yuhua Qian, Jiye Liang, Feng Wang 0038
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Parallel Communicating Grammar Systems with Incomplete Information Communication
Grammars, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Erzsébet Csuhaj-Varjú, György Vaszil
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The Closest Incomplete Medical Information Systems
2018 International Conference BIOMDLORE, 2018One of the main problem of medical information systems are incompleteness that may be caused by unreliable supplemented patient documentation, loss of part of documentation or errors during transferring information from paper to electronic documentation.
Katarzyna Ignatiuk, Agnieszka Dardzinska
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