scholarly journals N-Tier Soft Set Data Model: An Approach to Combine the Logicality of SQL and the Flexibility of NoSQL

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Jiang Wu ◽  
Du Ni ◽  
Zhi Xiao
Keyword(s):  
Set Theory ◽  
Data Model ◽  
Data Models ◽  
Soft Set ◽  
Relational Model ◽  
Relational Data ◽  
Huge Amount ◽  
Nosql Databases ◽  
Relational Data Model ◽  
Normative Perspective

To process a huge amount of data, computing resources need to be organized in clusters that can be scaled out easily. Still, traditional SQL databases built on the relational data model are difficult to be put to use in such clusters, which has motivated the movement named NoSQL. However, NoSQL databases have their limits by using their own data models. In this paper, the original soft set theory is extended, and a new theory system called n-tier soft set is brought up. We systematically constructed its concepts, definitions, and operations, establishing it as a novel soft set algebra. And some features of this algebra display its natural advantages as a data model which could combine the logicality of the SQL model (also known as the relational model) and the flexibility of NoSQL models. This data model provides a unified and normative perspective logic for organizing and manipulating data, combines metadata (semantic) and data to form a self-described structure, and combines index and data to realize fast locating and correlating.

Relational, Object-Oriented and Object-Relational Data Models

2005 ◽  
pp. 530-535
Author(s):  
Antonio Badia
Keyword(s):  
Programming Languages ◽  
Data Model ◽  
Query Language ◽  
Object Oriented ◽  
Data Models ◽  
Relational Model ◽  
Relational Data ◽  
Relational Data Model ◽  
Object Relational ◽  
Database Technology

The relational data model is the dominant paradigm in the commercial database market today, and it has been for several years. However, there have been challenges to the model over the years, and they have influenced its evolution and that of database technology. The object-oriented revolution that got started in programming languages arrived to the database area in the form of a brand new data model. The relational model managed not only to survive the newcomer but to continue becoming a dominant force, transformed into the object-relational model (also called extended relational, or universal) and relegating object-oriented databases to a niche product. Although this market has many nontechnical aspects, there are certainly important technical differences among the mentioned data models. In this article I describe the basic components of the relational, object-oriented, and object-relational data models. I do not, however, discuss query language, implementation, or system issues. A basic comparison is given and then future trends are discussed.

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.

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.

Relational Data Model

1996 ◽  
pp. 23-41 ◽  
Author(s):  
Paul Beynon-Davies
Keyword(s):  
Data Model ◽  
Relational Data ◽  
Relational Data Model
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