Handling complex and uncertain information in the ExIFO and NF2 data models

Date

1999-12-01

Author

Yazıcı, Adnan
Petry, FE

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

200
views

0
downloads

Trends in databases leading to complex objects present opportunities for representing imprecision and uncertainty that were difficult to integrate cohesively in simpler database models. In fact, one can begin at the conceptual level with a model that allows uncertainty assumptions and then transform those assumptions into a logical model having the necessary semantic foundations upon which to base a meaningful query language. Here we provide such a constructive approach beginning with the ExIFO model for expression of the conceptual design then show how the conceptual design is transformed into the logical design (which we utilize the extended NF2 logical database model). The steps are straightforward, unambiguous, and preserve the relevant information, including information concerning uncertainty.

Subject Keywords

conceptual modeling, Data management, Data models, Fuzzy sets, Uncertainty representation

URI

https://hdl.handle.net/11511/62844

Journal

IEEE TRANSACTIONS ON FUZZY SYSTEMS

DOI

https://doi.org/10.1109/91.811232

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Flexible querying in an intelligent object-oriented database environment Koyuncu, M; Yazıcı, Adnan; George, R (2000-10-28) Many new-generation database applications demand intelligent information management necessitating efficient interactions between database gr. knowledge bases and the users. In this study we discuss evaluation of imprecise queries in an intelligent object-oriented database environment, IFOOD. A flexible query evaluation mechanism, capable of handling different data types including complex and imprecise data and knowledge is presented and key language issues are addressed.
Reducing inconsistencies in intuitionistic 2-way adaptive fuzzy control systems Gurkan, E; Erkmen, Aydan Müşerref; Erkmen, İsmet (2000-08-30) Our objective in this paper is to model and reduce inconsistency in expert knowledge for our proposed 2-way adaptive fuzzy system that makes use of intuitionistic fuzzy sets. Intuitionistic fuzzy sets model an interval valued distribution of information in the adaptive control architecture with the necessity at the lower bound as the degree of membership functions and the possibility at the upper bound as the complement of the degree of nonmembership functions. Uncertainty is modelled as the width of this i...
Improving the scalability of ILP-based multi-relational concept discovery system through parallelization Mutlu, Ayşe Ceyda; Karagöz, Pınar; Kavurucu, Yusuf (2012-03-01) Due to the increase in the amount of relational data that is being collected and the limitations of propositional problem definition in relational domains, multi-relational data mining has arisen to be able to extract patterns from relational data. In order to cope with intractably large search space and still to be able to generate high-quality patterns. ILP-based multi-relational data mining and concept discovery systems employ several search strategies and pattern limitations. Another direction to cope w...
Modeling, inference and optimization of regulatory networks based on time series data Weber, Gerhard Wilhelm; DEFTERLİ, ÖZLEM; ALPARSLAN GÖK, Sırma Zeynep; Kropat, Erik (2011-05-16) In this survey paper, we present advances achieved during the last years in the development and use of OR, in particular, optimization methods in the new gene-environment and eco-finance networks, based on usually finite data series, with an emphasis on uncertainty in them and in the interactions of the model items. Indeed, our networks represent models in the form of time-continuous and time-discrete dynamics, whose unknown parameters we estimate under constraints on complexity and regularization by variou...
A fuzzy deductive object-oriented database model Bostan, B; Yazıcı, Adnan (1998-05-09) Object-oriented and deductive database models are two different paradigms in database modeling. As has been pointed out by many researchers, [1], [6], [14], each of these data models has its shortcomings when dealing with database/knowledge-base applications. Therefore, it is believed that combining object-oriented concepts with those of deductive database modeling results in a powerful data model especially for knowledge-intensive applications. In these applications, it is important to model and manipulate...

Citation Formats

A. Yazıcı and F. Petry, “Handling complex and uncertain information in the ExIFO and NF2 data models,” IEEE TRANSACTIONS ON FUZZY SYSTEMS, pp. 659–676, 1999, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62844.