Lossless join property in fuzzy relational databases

Bhuniya, B.; Niyogi, P.

doi:10.1016/0169-023x(93)90001-6

Cited by 31 publications

(10 citation statements)

References 17 publications

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“…Ƒ The definitions of FFDs vary with fuzzy data model. FFDs have been applied to redundancy elimination [24,25], fuzzy data compression [26], and lossless join decomposition [27]. In the EPFR data model, the definitions of FFDs are also of variety in literature [28,29].…”

Section: It Is Noted That Two Different Valuesmentioning

confidence: 99%

Handling Missing Data in Extended Possibility-based Fuzzy Relational Databases

Liu

Huang

2012

2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications

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Handling missing data is widely studied to make proper replacement and reduce uncertainty of data. Several approaches have been proposed for providing the most possible results. However, few studies provide solutions to the problem of missing data in extended possibility-based fuzzy relational (EPFR) databases. This type of problem in the context of EPFR databases is difficult to resolve because of the complexity of the data involved. In this paper, we propose an approach of filling missing data and query processing of the databases. To obtain the rational predict of the missing data, we adopt a concept and measurement of proximate equality of tuples to define data operation and fuzzy functional dependency (FFD). We provide a method to predict the missing data and replace the data based on our proposal. The results of the missing value process preserve those FFDs that hold in the original database instance.

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Section: It Is Noted That Two Different Valuesmentioning

confidence: 99%

Handling Missing Data in Extended Possibility-based Fuzzy Relational Databases

Liu

Huang

2012

2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications

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“…In literature, various FFDs are defined for different fuzzy data model. For some fuzzy data representation, FFDs are defined based on the equivalence classes of tuples, such as the similarity-based fuzzy data model [33]. In the extended possibility-based databases, the definition of FFD is also of variety, such as literature [10,14,34,35].…”

Section: The Fuzzy Databasesmentioning

confidence: 99%

Lossless Join Decomposition for Extended Possibility-Based Fuzzy Relational Databases

Liu

2014

Journal of Applied Mathematics

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Functional dependency is the basis of database normalization. Various types of fuzzy functional dependencies have been proposed for fuzzy relational database and applied to the process of database normalization. However, the problem of achieving lossless join decomposition occurs when employing the fuzzy functional dependencies to database normalization in an extended possibilitybased fuzzy data models. To resolve the problem, this study defined fuzzy functional dependency based on a notion of approximate equality for extended possibility-based fuzzy relational databases. Examples show that the notion is more applicable than other similarity concept to the research related to the extended possibility-based data model. We provide a decomposition method of using the proposed fuzzy functional dependency for database normalization and prove the lossless join property of the decomposition method.

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“…where θ ∈ [0, 1] is an additional parameter. The basic relationship to our approach is the following: A similar approach is adopted in (Bhuniya & Niyogi, 1993). In (Chen, Kerre, & Vandenbulcke, 1994),…”

Section: Overview Of Approaches To Functional Dependen-cies In Codd'smentioning

confidence: 99%

Data Dependencies in Codd's Relational Model with Similarities

Bělohlávek

Vychodil

2008

Handbook of Research on Fuzzy Information Processing in Databases

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This chapter deals with data dependencies in Codd's relational model of data. In particular, we deal with fuzzy logic extensions of the relational model which consist in adding similarity relations to domains and consider functional dependencies in these extensions. We present a particular extension and functional dependencies in this extension which follows the principles of fuzzy logic in narrow sense. We present selected features and results regarding this extension. Then, we use this extension as a reference model and compare it to several other extensions proposed in the literature. We argue that following the principles of fuzzy logic in narrow sense the same way as following the principles of classical logic in case of ordinary Codd's relational model helps achieve transparency, versatility, conceptual clarity, and theoretical and computational tractability of the extension. We outline several topics for future research.

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Lossless join property in fuzzy relational databases

Cited by 31 publications

References 17 publications

Handling Missing Data in Extended Possibility-based Fuzzy Relational Databases

Handling Missing Data in Extended Possibility-based Fuzzy Relational Databases

Lossless Join Decomposition for Extended Possibility-Based Fuzzy Relational Databases

Data Dependencies in Codd's Relational Model with Similarities

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