The Experts below are selected from a list of 126 Experts worldwide ranked by ideXlab platform
S.k.m. Wong - One of the best experts on this subject based on the ideXlab platform.
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An Extended Relational Data Model For Probabilistic Reasoning
Journal of Intelligent Information Systems, 1997Co-Authors: S.k.m. WongAbstract:Probabilistic methods provide a formalism for reasoning aboutpartial beliefs under conditions of uncertainty. This paper suggests a newrepresentation of probabilistic knowledge. This representation encompassesthe traditional relational database model. In particular, it is shown thatprobabilistic conditional independence is equivalent to the notion of generalized Multivalued Dependency. More importantly,a Markov network can be viewed as a generalized acyclic joinDependency. This linkage between these two apparently different butclosely related knowledge representations provides a foundation fordeveloping a unified model for probabilistic reasoning and relationaldatabase systems.
Ranjit Biswas - One of the best experts on this subject based on the ideXlab platform.
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intuitionistic fuzzy Multivalued Dependency and intuitionistic fuzzy fourth normal form
FICTA, 2016Co-Authors: Asma R Shora, Afshar M Alam, Ranjit BiswasAbstract:Intuitionistic fuzzy databases are used to handle imprecise and uncertain data as they represent the membership, nonmembership, and hesitancy associated with a certain element in a set. This paper presents the Intuitionistic Fuzzy Fourth Normal Form to decompose the Multivalued dependent data. A technique to determine Intuitionistic Fuzzy Multivalued dependencies by working on the closure of dependencies has been proposed. We derive the closure by obtaining all the logically implied dependencies by a set of Intuitionistic Fuzzy Multivalued dependencies, i.e., Inference Rules. A complete set of inference rules for the Intuitionistic Fuzzy Multivalued dependencies has been given along with the derivation of each rule. These rules help us to compute the Dependency closure and we further use the same for defining the Intuitionistic Fuzzy Fourth Normal Form.
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FICTA - Intuitionistic Fuzzy Multivalued Dependency and Intuitionistic Fuzzy Fourth Normal Form
Advances in Intelligent Systems and Computing, 2015Co-Authors: Asma R Shora, M. Afshar Alam, Ranjit BiswasAbstract:Intuitionistic fuzzy databases are used to handle imprecise and uncertain data as they represent the membership, nonmembership, and hesitancy associated with a certain element in a set. This paper presents the Intuitionistic Fuzzy Fourth Normal Form to decompose the Multivalued dependent data. A technique to determine Intuitionistic Fuzzy Multivalued dependencies by working on the closure of dependencies has been proposed. We derive the closure by obtaining all the logically implied dependencies by a set of Intuitionistic Fuzzy Multivalued dependencies, i.e., Inference Rules. A complete set of inference rules for the Intuitionistic Fuzzy Multivalued dependencies has been given along with the derivation of each rule. These rules help us to compute the Dependency closure and we further use the same for defining the Intuitionistic Fuzzy Fourth Normal Form.
Asma R Shora - One of the best experts on this subject based on the ideXlab platform.
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intuitionistic fuzzy Multivalued Dependency and intuitionistic fuzzy fourth normal form
FICTA, 2016Co-Authors: Asma R Shora, Afshar M Alam, Ranjit BiswasAbstract:Intuitionistic fuzzy databases are used to handle imprecise and uncertain data as they represent the membership, nonmembership, and hesitancy associated with a certain element in a set. This paper presents the Intuitionistic Fuzzy Fourth Normal Form to decompose the Multivalued dependent data. A technique to determine Intuitionistic Fuzzy Multivalued dependencies by working on the closure of dependencies has been proposed. We derive the closure by obtaining all the logically implied dependencies by a set of Intuitionistic Fuzzy Multivalued dependencies, i.e., Inference Rules. A complete set of inference rules for the Intuitionistic Fuzzy Multivalued dependencies has been given along with the derivation of each rule. These rules help us to compute the Dependency closure and we further use the same for defining the Intuitionistic Fuzzy Fourth Normal Form.
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FICTA - Intuitionistic Fuzzy Multivalued Dependency and Intuitionistic Fuzzy Fourth Normal Form
Advances in Intelligent Systems and Computing, 2015Co-Authors: Asma R Shora, M. Afshar Alam, Ranjit BiswasAbstract:Intuitionistic fuzzy databases are used to handle imprecise and uncertain data as they represent the membership, nonmembership, and hesitancy associated with a certain element in a set. This paper presents the Intuitionistic Fuzzy Fourth Normal Form to decompose the Multivalued dependent data. A technique to determine Intuitionistic Fuzzy Multivalued dependencies by working on the closure of dependencies has been proposed. We derive the closure by obtaining all the logically implied dependencies by a set of Intuitionistic Fuzzy Multivalued dependencies, i.e., Inference Rules. A complete set of inference rules for the Intuitionistic Fuzzy Multivalued dependencies has been given along with the derivation of each rule. These rules help us to compute the Dependency closure and we further use the same for defining the Intuitionistic Fuzzy Fourth Normal Form.
Cory J. Butz - One of the best experts on this subject based on the ideXlab platform.
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IICAI - On the Practical Irrelevance of Diverging Implication between Probabilistic Conditional Independence and Embedded Multivalued Dependency.
2020Co-Authors: Cory J. Butz, Pawan LingrasAbstract:Bayesian networks serve as the basis for developing probabilistic expert systems and have been applied widely in artificial intelligence. Previous research has argued that Bayesian networks and relational databases are different by showing that the logical implication of conditional independence (CI) and embedded Multivalued Dependency (EMVD) do not always coincide. In this paper, we show that this theoretical difference has no practical impact when designing probabilistic expert systems. Therefore, this work adds to the mounting evidence clearly indicating that the implementation of probabilistic expert systems can take advantage of conventional relational database management systems.
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The relational database theory of bayesian networks
2020Co-Authors: Michael S. K. M. Wong, Cory J. ButzAbstract:Based on the elegant theory of relational databases, the present investigation establishes a unified model for both relational databases and Bayesian networks. This is in contradiction to the argument that relational databases and Bayesian networks are different, where it was shown that the implication problem does not coincide for embedded Multivalued Dependency (EMVD) and probabilistic conditional independence (CI). The main result of this thesis, however, is that the implication problem coincides on the solvable subclasses of EMVD and CI, but differs on the unsolvable general classes of EMVD and CI. This means that there is no practical difference between relational databases and Bayesian networks, since only the solvable subclasses are useful in the design of both of these knowledge systems. A unified model provides the opportunity for cross-research. Recently, attempts have been made to generalize the standard Bayesian network model with contextual dependencies, an object-oriented Bayesian network model, and a multi-agent Bayesian network model. In this thesis, we demonstrate the usefulness of our unified model by making significant contributions to these extensions. By drawing from the highly developed relational database model, we propose more general probabilistic dependencies as well as several consistency results in the object-oriented and multi-agent models.
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on the practical irrelevance of diverging implication between probabilistic conditional independence and embedded Multivalued Dependency
Indian International Conference on Artificial Intelligence, 2005Co-Authors: Cory J. Butz, Pawan LingrasAbstract:Bayesian networks serve as the basis for developing probabilistic expert systems and have been applied widely in artificial intelligence. Previous research has argued that Bayesian networks and relational databases are different by showing that the logical implication of conditional independence (CI) and embedded Multivalued Dependency (EMVD) do not always coincide. In this paper, we show that this theoretical difference has no practical impact when designing probabilistic expert systems. Therefore, this work adds to the mounting evidence clearly indicating that the implementation of probabilistic expert systems can take advantage of conventional relational database management systems.
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Rough Sets and Current Trends in Computing - Properties of Weak Conditional Independence
Lecture Notes in Computer Science, 2002Co-Authors: Cory J. Butz, Manon J. SanscartierAbstract:Object-oriented Bayesian networks (OOBNs) facilitate the design of large Bayesian networks by allowing Bayesian networks to be nested inside of one another. Weak conditional independence has been shown to be a necessary and sufficient condition for ensuring consistency in OOBNs. Since weak conditional independence plays such an important role in OOBNs, in this paper we establish two useful results relating weak conditional independence with weak Multivalued Dependency in relational databases. The first result strengthens a previous result relating conditional independence and Multivalued Dependency. The second result takes a step towards showing that the complete axiomatization for weak Multivalued Dependency is also complete for full weak conditional independence.
Dzenan Gusic - One of the best experts on this subject based on the ideXlab platform.
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Continuous Maps in Fuzzy Relations
WSEAS Transactions on Systems and Control archive, 2020Co-Authors: Dzenan GusicAbstract:In this paper we generalize our most recent results that are related to the algorithm that has been developed to automatically derive a fuzzy functional or a fuzzy Multivalued Dependency from a given set of fuzzy functional and fuzzy Multivalued dependencies. Fuzzy dependencies are considered as fuzzy formulas. The first result states that a two-element fuzzy relation instance actively satisfies a fuzzy Multivalued Dependency if and only if the tuples of the instance are conformant on some known set of attributes with degree of conformance larger than some known constant, and the corresponding fuzzy formula is valid in appropriate interpretations. The second result states that a fuzzy functional or a fuzzy Multivalued Dependency follows from a set of fuzzy functional and fuzzy Multivalued dependencies in two-element fuzzy relation instances if and only if the corresponding fuzzy formula is a logical consequence of the corresponding set of fuzzy formulas. Our earlier research in this direction consisted in an application of some individual fuzzy implication operator, such as Yager, Reichenbach, Kleene-Dienes fuzzy implication operator. The main purpose of this paper is to prove that the aforementioned results remain valid for a wider class of fuzzy implication operators, in particular for the family of f-generated fuzzy implication operators
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On Completeness of Inference Rules for Vague Functional and Vague Multivalued Dependencies in Two-Element Vague Relation
WSEAS Transactions on Systems and Control archive, 2020Co-Authors: Dzenan GusicAbstract:In this paper we pay attention to completeness of the inference rules for vague functional and vague Multivalued dependencies in two-element, vague relation instances. Motivated by the fact that the set of the inference rules is a complete set, that is, these exists a vague relation instance on given relation scheme which satisfies all vague functional and vague Multivalued dependencies in the closure of the union of some set of vague functional and some set of vague Multivalued dependencies, and violates a vague functional, respectively, a vague Multivalued Dependency outside of the closure, we prove that the vague relation instance may be chosen to contain only two elements.
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New Vague Dependencies as a Result of Automatization
WSEAS Transactions on Systems and Control archive, 2020Co-Authors: Dzenan Gusic, Sanela NesimovicAbstract:Today, higher output and increased productivity are two of the biggest reasons in justifying the use of automatization. It is involved in each aspect of life and human activity. The same is true of science. In this paper we consider generalized functional and Multivalued dependencies, that is, vague functional and vague Multivalued dependencies. We consider both types as fuzzy formulas. We provide very strict proof of the equivalence: any two-element vague relation instance on given scheme (which satisfies some set of vague functional and vague Multivalued dependencies) satisfies given vague functional or vague Multivalued Dependency if and only if the joined fuzzy formula is a logical consequence of the corresponding set of fuzzy formulas. This result represents natural continuation and a generalization of our recent study where we were particularly interested in vague functional dependencies. The key role of such results is to encourage automatically checking if some vague Dependency (functional or Multivalued) follows from some set of vague dependencies (functional and Multivalued). An example which includes both kinds of vague dependencies is also given
