A New Functional Dependency in a Vague Relational Database Model

Mishra, Jaydev; Ghosh, Sharmistha

doi:10.5120/4841-7104

Cited by 12 publications

(3 citation statements)

References 18 publications

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“…Some other models (e.g. [11], [13], [14], [15], [21], [23]) using only the fuzzy set theory could express and manipulate imprecise information but not uncertain information of objects.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Relational Database Model for Uncertain and Imprecise Information

Nguyen

2020

AJCIS

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Recent years, many fuzzy or probabilistic database models have been built for representing and handling imprecise or uncertain information of objects in real-world applications. However, relational database models combining the relevance and strength of both fuzzy set and probability theories have rarely been proposed. This paper introduces a new relational database model, as a hybrid one combining consistently fuzzy set theory and probability theory for modeling and manipulating uncertain and imprecise information, where the uncertainty and imprecision of a relational attribute value are represented by a fuzzy probabilistic triple, the computation and combination of relational attribute values are implemented by using the probabilistic interpretation of binary relations on fuzzy sets, and the elimination of redundant data is dealt with by coalescing e-equivalent tuples. The basic concepts of the classical relational database model are extended in this new model. Then the relational algebraic operations are formally defined accordingly. A set of the properties of the relational algebraic operations is also formulated and proven.

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“…Some other models (e.g. [11], [13], [14], [15], [21], [23]) using only the fuzzy set theory could express and manipulate imprecise information but not uncertain information of objects.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Relational Database Model for Uncertain and Imprecise Information

Nguyen

2020

AJCIS

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“…Some cases it is very difficult to solve using fuzzy logic [1,2,3]. The vague set [5,6,7] is handled the uncertain data using truth and false values. Fuzzy and vague both data cannot be represented as three membership value, so they are unable to process uncertain data which is consists of true, indeterminacy and false, these three values.…”

Section: Introductionmentioning

confidence: 99%

To Handle Uncertain Data for Medical Diagnosis Purpose Using Neutrosophic Set

De¹,

Mishra²

2017

2017 IEEE 7th International Advance Computing Conference (IACC)

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Here we proposed a new approach which is based on Neutrosophic logic to help the patient by taking proper decision through pathological report based analysis. Neutrosophic set is used for uncertain data. Fuzzy data is used to handle incomplete data by only truth value and vague data is applicable for uncertain data by truth and false values. But both are unable to handle uncertain data for any analytical based system. Now neutrosophic set is being used to handling uncertain data in the form of neutrosophic data for pathological test report based decision making operation. Keywords-neutrosophic set; neutrosophic data; membership domain; equation of neutrosophic data; new approach of decision making.

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“…A large number of another relational database models has been built based on the fuzzy set theory for modeling objects about which information may be vague. These model sare called fuzzy relational database models [5,[12][13][14][15] and they did not represent and handle probabilistic information of objects.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Probabilistic Relational Database Model and Algebra

Nguyen¹

2016

JCC

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This paper introduces a complete extended relational database model based on probability theory and fuzzy theory, called FPRDB, for representing and handling vague and uncertain information of objects in real world applications. FPRDB is built by first extending probabilistic triples with fuzzy sets, associating the probability of such fuzzy sets for expressing and computing uncertain degree of imprecise information. Then, schemas, fuzzy probabilistic relations, fuzzy probabilistic functional dependencies and algebraic operations are defined coherently and consistently for FPRDB. A set of the properties of the relational algebraic operations in FPRDB is also formulated and proven.

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A New Functional Dependency in a Vague Relational Database Model

Cited by 12 publications

References 18 publications

A Hybrid Relational Database Model for Uncertain and Imprecise Information

A Hybrid Relational Database Model for Uncertain and Imprecise Information

To Handle Uncertain Data for Medical Diagnosis Purpose Using Neutrosophic Set

A Fuzzy Probabilistic Relational Database Model and Algebra

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