scholarly journals Lossless Join Decomposition for Extended Possibility-Based Fuzzy Relational Databases

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Julie Yu-Chih Liu
Keyword(s):  
Relational Database ◽  
Data Model ◽  
Decomposition Method ◽  
Relational Databases ◽  
Data Models ◽  
Functional Dependency ◽  
Fuzzy Data ◽  
Functional Dependencies ◽  
Approximate Equality ◽  
Database Normalization

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 possibility-based 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.

Handling Imprecise and Uncertain Engineering Information in IDEF1X and Relational Data Models

2004 ◽  
Author(s):  
Z. M. Ma
Keyword(s):  
Relational Database ◽  
Data Model ◽  
Manufacturing Systems ◽  
Relational Databases ◽  
Manufacturing Industries ◽  
Fuzzy Data ◽  
Uncertain Information ◽  
Database Modeling ◽  
Engineering Information ◽  
Conceptual Data

Database modeling of engineering information is crucial for constructing manufacturing systems because current manufacturing industries are typically information-based enterprises and information systems have become their nervous center. Engineering information can be modeled at two levels: conceptual data model and logical database model. Generally a conceptual data model is designed and then the designed conceptual data model will be transformed into the chosen logical database schema. Imprecise and uncertain information, however, is generally involved in many engineering activities and imprecise and uncertain engineering information are represented by fuzzy sets. Nowadays relational databases are still the most useful database product and IDEF1X is most useful for logical database design of relational databases in engineering. So in this paper, we focus on fuzzy data modeling in IDEF1X and relational databases. The formal approaches to mapping fuzzy IDEF1X models to fuzzy relational database schemes are hereby developed.

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.

scholarly journals FORMALIZED REPRESENTATION FOR THE DATA MODEL WITH THE UNIVERSAL BASIS OF RELATIONS

2019 ◽  
pp. 453-460
Author(s):  
Vitalii I. Yesin ◽  
Mikolaj Karpinski ◽  
Maryna V. Yesina ◽  
Vladyslav V. Vilihura
Keyword(s):  
Relational Database ◽  
Data Model ◽  
Relational Databases ◽  
Subject Domain ◽  
Dynamic Changes ◽  
Standard Data ◽  
The Subject ◽  
Logical Design ◽  
The Creation ◽  
Universal Standard

The goal of the article is to develop a universal (standard) data model that allows you to get rid of the need for a costly policy of doing extra work when developing new ones or transforming existing relational databases (RDBs) caused by dynamic changes in the subject domain (SD). The requirements for the developed data model were formulated. In accordance with the formulated requirements, the data model was synthesized. To simplify the process of creating relational database schemas an algorithm for transforming the description of the subject domain into the relations of the universal basis of the developed model was proposed. The scientific novelty of the obtained results is: a data model that, unlike known ones, allows us to simplify the creation of RDB schemas at the stage of logical design of relational databases, under the conditions of dynamic changes in subject domains, due to the introduced universal basis of relations, as a means of describing structures and the presentation of data for various SDs has been developed.

scholarly journals Content enrichment with expressive document modelling to leverage the understanding of unstructured data

2019 ◽  
Vol 277 ◽  
pp. 02003
Author(s):  
Ganesh Selvaraj ◽  
Karla Taboada ◽  
Eloy Gonzales ◽  
Habib Baluwala
Keyword(s):  
Data Model ◽  
Relational Databases ◽  
Data Models ◽  
Temporal Information ◽  
Unstructured Data ◽  
Dublin Core ◽  
Provenance Information ◽  
Document Database ◽  
Generalize Information

Most information in an enterprise is in the form of unstructured data which is usually managed using a document database. One of the key challenges is to define a generalized data model for this unstructured data and any information extracted from it using content enrichment algorithms. It is more challenging to incorporate provenance and temporal capabilities to such data models. Semantic databases use ontologies such as PROV-O to represent their provenance information expressively, and relational databases use for example Slowly Changing Dimensions (SCDs) concepts to represent temporal information. In this paper, we present a document model which has features inspired from Dublin core, PROV-O and temporal methodologies to generalize information extracted from unstructured data using content enrichment algorithms. Provenance information enables comparison of enrichment models, allows reproducibility and facilitates complex filtering on the enriched data. Temporal metadata helps in versioning the document and enables point-intime and history queries conveniently.

Modeling Fuzzy Information in the IFO and Relational Data Model

2011 ◽  
pp. 273-293
Author(s):  
Z. M. Ma
Keyword(s):  
Fuzzy Set ◽  
Relational Database ◽  
Real World ◽  
Data Model ◽  
Data Models ◽  
Computer Applications ◽  
Relational Data Model ◽  
Relational Database Schema ◽  
Conceptual Data ◽  
Design Databases

Computer applications in non-traditional areas have put requirements on conceptual data modeling. Some conceptual data models, being the tool of design databases, have been proposed. However, information in real-world applications is often vague or ambiguous. Currently, less research has been done in modeling imprecision and uncertainty in conceptual data models and the design of databases with imprecision and uncertainty. In this chapter, a different level of fuzziness based on fuzzy set and possibility distribution theory will be introduced into the IFO data model and the corresponding graphical representations will be given. The IFO data model is then extended to a fuzzy IFO data model, denoted IF2O. In particular, we provide the approach to mapping an IF2O model to a fuzzy relational database schema.

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.

CREATING SQL-QUERIES IN RELATIONAL DATABASES

2020 ◽  
pp. 8-19
Author(s):  
Sapiahon Khaidarova ◽  
Keyword(s):  
Relational Database ◽  
Data Model ◽  
Relational Databases ◽  
Query Language ◽  
Information Model ◽  
Subject Area ◽  
Conceptual Level ◽  
Microsoft Access ◽  
Pedagogical Institute ◽  
The Subject

The article outlines the methods for creating SQL queries in relational databases. The use of the structured query language SQL in relational databases is substantiated. It provides information about the SQL standard and the three-tier database organization system. The author describes the choice of a data model based on the conceptual level using to that end an example of the Kokand Pedagogical Institute as the relational database model. A relational conceptual diagram of the information model of a pedagogical institute is compiled. Such a conceptual diagram is depicted using a cluster. Objects of the subject area are depicted in the form of tables, which differ from each other in geometric shapes or colors. The relationships between tables in Microsoft Access are presented. The basic rules for creating and filling tables in SQL using the instructions CREATE TABLE and INSERT INTO are considered. The syntax of the SELECT statement is given. All offers of the SELECT statement and their order are listed. Examples are given for compiling simple queries and subqueries in SQL using the SELECT statement for the database of the Kokand Pedagogical Institute. Information about the order of execution of internal and external requests is given. The article considers the ORDER BY offer of a SELECT statement for sorting query results.

Dynamic Discovery of Fuzzy Functional Dependencies Using Partitions

2010 ◽  
pp. 44-60
Author(s):  
Shyue-Liang Wang ◽  
Ju-Wen Shen ◽  
Tuzng-Pei Hong
Keyword(s):  
Relational Databases ◽  
Computation Time ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Functional Dependencies ◽  
Mining Algorithm ◽  
Analysis Technique ◽  
Static Data ◽  
Pair Wise Comparison ◽  
Reduce Computation Time

Discovery of functional dependencies (FDs) from relational databases has been identified as an important database analysis technique. Various mining techniques have been proposed in recent years to deal with crisp and static data. However, few have emphasized on fuzzy data and also considered the dynamic nature that data may change all the time. In this work, the authors propose a partition-based incremental data mining algorithm to discover fuzzy functional dependencies from similarity-based fuzzy relational databases when new sets of tuples are added. Based on the concept of tuple partitions and the monotonicity of fuzzy functional dependencies, we avoid re-scanning of the database and thereby reduce computation time. An example demonstrating the proposed algorithm is given. Computational complexity of the proposed algorithm is analyzed. Comparison with pair-wise comparison-based incremental mining algorithm (Wang, Shen & Hong, 2000) is presented. It is shown that with certain space requirement, partition-based approach is more time efficient than pair-wise approach in the discovery of fuzzy functional dependencies dynamically.

Which Way to Go for the Future

2018 ◽  
pp. 311-328
Keyword(s):  
Big Data ◽  
Relational Database ◽  
Data Model ◽  
Driving Force ◽  
Relational Databases ◽  
High Availability ◽  
Nosql Databases ◽  
The Future ◽  
Data Requirements ◽  
Open Issues

The chapter presents how relational databases answer to typical NoSQL features, and, vice versa, how NoSQL databases answer to typical relational features. Open issues related to the integration of relational and NoSQL databases, as well as next database generation features are discussed. The big relational database vendors have continuously worked to incorporate NoSQL features into their databases, as well as NoSQL vendors are trying to make their products more like relational databases. The convergence of these two groups of databases has been a driving force in the evolution of database market, in establishing a new level of focus to resolving big data requirements, and in enabling users to fully use data potential, wherever data is stored, in relational or NoSQL databases. In turn, the database of choice in the future will likely be one that provides the best of both worlds: flexible data model, high availability, and enterprise reliability.

scholarly journals Using the REA Data Model in Order to Reduce Design Complexity in an Inventory Management Relational Database, and the Need for a Relational Database Complexity Metric.

2021 ◽  
Vol 9 (7) ◽  
pp. 71-78
Author(s):  
Ian Adamson
Keyword(s):  
Inventory Management ◽  
Relational Database ◽  
Data Model ◽  
Relational Databases ◽  
Database Design ◽  
Business Environment ◽  
Design Complexity ◽  
Data Inconsistency ◽  
A Company

With the extensive use of relational databases in the business environment there is a need to reduce database complexity in order to avoid data inconsistency and redundancy, which can provide a company with unreliable and/or meaningless data and information. The use of the REA Data Model in database design can significantly help with this problem.  The model can eliminate the need for unnecessary data artifacts which should only be generated by the system when needed. This paper also addresses the need for a Relational Database Complexity Metric. A simple and easy to understand metric is presented.

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