scholarly journals On the equivalent descriptions of family of functional dependencies in the relational data model

2016 ◽  
Vol 11 (4) ◽  
pp. 35-45
Author(s):  
Vũ Đức Thi
Keyword(s):  
Data Model ◽  
Functional Dependency ◽  
Relational Data ◽  
Functional Dependencies ◽  
Relational Data Model ◽  
Design And Implementation ◽  
Closure Operations ◽  
The Family ◽  
Equivalent Description

The family of functional dependencies (FDs) was introduced by E.F. Codd. Equivalent descriptions of family of FDs play essential rules in the design and implementation of the relation datamodel. It is known [1,3,4,5,7,8,12,13,15] that closure operations, meet-semilattices, families of members which are not intersections of two other members give the equivalent descriptions of family of FDs. i.e. they and family of  FDs determine each other uniquely. These equivalent description were  successfully applied to find many desirable properties of  functional dependency. This paper introduces the concept of maximal family of attributes. We prove that this family is an equivalent description of family of FDs. The concept of nonredundant family of attributes is also introduced in this paper. We present some characterizations and desirable properties of these families.

Normalization based on fuzzy functional dependency in a fuzzy relational data model

1996 ◽  
Vol 21 (3) ◽  
pp. 299-310 ◽  
Author(s):  
Guoqing Chen ◽  
Etienne E. Kerre ◽  
Jacques Vandenbulcke
Keyword(s):  
Data Model ◽  
Functional Dependency ◽  
Relational Data ◽  
Relational Data Model

Integration of the family of extended description logic languages with relational data model

2016 ◽  
pp. 032-037
Author(s):  
V.A. Reznichenko ◽  
◽  
I.S. Chystiakova ◽  
Keyword(s):  
Data Structure ◽  
Data Model ◽  
Description Logic ◽  
Relational Data ◽  
Data Manipulation ◽  
Relational Data Model ◽  
The Family ◽  
The Creation

The paper is a logical continuation of the previously published work, which was dedicated to the creation of the data manipulation methods. Based on the previously created binary relational data structure we perform mappings of the ALC extension into relational data model (RDM). The results of previous research namely data structure RM2 and mappings of the basic ALC concepts into RDM was used in this paper.

Multivalued Dependencies in Fuzzy Multivalued Relational Databases Using Fuzzy Functions

2015 ◽  
Vol 23 (04) ◽  
pp. 589-626 ◽  
Author(s):  
Devendra K. Tayal ◽  
P. C. Saxena
Keyword(s):  
Real World ◽  
Data Model ◽  
Relational Databases ◽  
Relational Model ◽  
Relational Data ◽  
Inference Rules ◽  
Functional Dependencies ◽  
Technical Institute ◽  
Relational Schema ◽  
Relational Data Model

In this paper we discuss an important integrity constraint called multivalued dependency (mvd), which occurs as a result of the first normal form, in the framework of a newly proposed model called fuzzy multivalued relational data model. The fuzzy multivalued relational data model proposed in this paper accommodates a wider class of ambiguities by representing the domain of attributes as a “set of fuzzy subsets”. We show that our model is able to represent multiple types of impreciseness occurring in the real world. To compute the equality of two fuzzy sets/values (which occur as tuple-values), we use the concept of fuzzy functions. So the main objective of this paper is to extend the mvds in context of fuzzy multivalued relational model so that a wider class of impreciseness can be captured. Since the mvds may not exist in isolation, a complete axiomatization for a set of fuzzy functional dependencies (ffds) and mvds in fuzzy multivalued relational schema is provided and the role of fmvds in obtaining the lossless join decomposition is discussed. We also provide a set of sound Inference Rules for the fmvds and derive the conditions for these Inference Rules to be complete. We also derive the conditions for obtaining the lossless join decomposition of a fuzzy multivalued relational schema in the presence of the fmvds. Finally we extend the ABU's Algorithm to find the lossless join decomposition in context of fuzzy multivalued relational databases. We apply all of the concepts of fmvds developed by us to a real world application of “Technical Institute” and demonstrate that how the concepts fit well to capture the multiple types of impreciseness.

Answering Null Queries by Analogical Reasoning on Similarity-based Fuzzy Relational Databases

2001 ◽  
Vol 5 (3) ◽  
pp. 163-171
Author(s):  
Shyue-Liang Wang ◽  
◽  
Tzung-Pei Hong ◽  
Wen-Yang Lin ◽  
Keyword(s):  
Data Model ◽  
Relational Databases ◽  
Analogical Reasoning ◽  
Fuzzy Model ◽  
Functional Dependency ◽  
Relational Data ◽  
Relational Data Model ◽  
Equality Relation ◽  
Approximate Answers ◽  
Fuzzy Equality

We present here a method of using analogical reasoning to infer approximate answers for null queries on similarity-based fuzzy relational databases. Null queries are queries that elicit a null answer from a database. Analogical reasoning assumes that if two situations are known to be similar in some respects, it is likely that they will be similar in others. Application of analogical reasoning to infer approximate answers for null queries using fuzzy functional dependency and fuzzy equality relation on possibility-based fuzzy relational database has been studied. However, the problem of inferring approximate answers has not been fully explored on the similarity-based fuzzy relational data model. In this work, we introduce the concept of approximate dependency and define a similarity measure on the similaritybased fuzzy model, as extensions to the fuzzy functional dependency and fuzzy equality relation respectively. Under the framework of reasoning by analogy, our method provides a flexible query answering mechanism for null queries on the similarity-based fuzzy relational data model.

scholarly journals Storing Hypergraph-Based Data Models in Non-hypergraph Data Storage and Applications for Information Systems

2021 ◽  
pp. 1-21
Author(s):  
Bálint Molnár ◽  
András Béleczki ◽  
Bence Sarkadi-Nagy
Keyword(s):  
Information Systems ◽  
Data Storage ◽  
Data Model ◽  
Relational Data ◽  
Complex Data ◽  
Graph Representations ◽  
Relational Data Model ◽  
Rich Information ◽  
The Relationship

Data structures and especially the relationship among the data entities have changed in the last couple of years. The network-like graph representations of data-model are becoming more and more common nowadays, since they are more suitable to depict these, than the well-established relational data-model. The graphs can describe large and complex networks — like social networks — but also capable of storing rich information about complex data. This was mostly of relational data-model trait before. This also can be achieved with the use of the knowledge representation tool called “hypergraphs”. To utilize the possibilities of this model, we need a practical way to store and process hypergraphs. In this paper, we propose a way by which we can store hypergraphs model in the SAP HANA in-memory database system which has a “Graph Core” engine besides the relational data model. Graph Core has many graph algorithms by default however it is not capable to store or to work with hypergraphs neither are any of these algorithms specifically tailored for hypergraphs either. Hence in this paper, besides the case study of the two information systems, we also propose pseudo-code level algorithms to accommodate hypergraph semantics to process our IS model.

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