scholarly journals Transformation of Schema from Relational Database (RDB) to NoSQL Databases

Data ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 148 ◽  
Author(s):  
Obaid Alotaibi ◽  
Eric Pardede
Keyword(s):  
Relational Database ◽  
Proof Of Concept ◽  
Current Standard ◽  
Nosql Databases ◽  
Transformation Rules ◽  
Nosql Database ◽  
It Applications ◽  
Increasing Demand ◽  
Existing Data

Relational database has been the de-facto database choice in most IT applications. In the last decade there has been increasing demand for applications that have to deal with massive and un-normalized data. To satisfy the demand, there is a big shift to use more relaxed databases in the form of NoSQL databases. Alongside with this shift, there is a need to have a structured methodology to transform existing data in relational database (RDB) to NoSQL database. The transformation from RDB to NoSQL database has become more challenging because there is no current standard on NoSQL database. The aim of this paper is to propose transformation rules of RDB Schema to various NoSQL database schema, namely document-based, column-based and graph-based databases. The rules are applied based on the type of relationships that can appear in data within a database. As a proof of concept, we apply the rules into a case study using three NoSQL databases, namely MongoDB, Cassandra, and Neo4j. A set of queries is run in these databases to demonstrate the correctness of the transformation results. In addition, the completeness of our transformation rules are compared against existing work.

Bridging Relational and NoSQL Worlds

2018 ◽  
pp. 282-310
Keyword(s):  
Relational Database ◽  
Real Case ◽  
Environmental Standards ◽  
Oracle Database ◽  
Native Solution ◽  
Nosql Databases ◽  
Nosql Database ◽  
Vehicle Registration ◽  
Hybrid Solution

The chapter presents a real case study of the integration of relational and NoSQL databases. The example of a real project related to vehicle registration, particularly to testing vehicles for compliance with environmental standards, explains how those two worlds can be integrated. Oracle database is used as a relational database, while MongoDB is used as NoSQL database. The chapter sustains that the COMN notation can be successfully used in the process of modeling both relational and nonrelational data. All three ways of integration of relational and NoSQL databases are tested. The native solution was tested by using of native drivers for communication with Oracle and MongoDB databases. The hybrid solution used a Unity product. The reducing-to-one option, in this case, SQL, was tested on Oracle database. The capabilities of Oracle 12c database to work both with relational and nonrelational data by using SQL were tested.

Comparative Case Study Difference Between Azure Cloud SQL and Mongo Atlas MongoDB NoSQL Database

2021 ◽  
Vol 9 (7) ◽  
pp. 999-1002
Keyword(s):  
Relational Database ◽  
Database Management ◽  
Database Management Systems ◽  
Management Systems ◽  
Comparative Case Study ◽  
Nosql Databases ◽  
Nosql Database ◽  
Relational Database Management ◽  
Relational Database Management Systems

Azure SQL and Atlas Mongodb NoSQL(Azure instance) databases are the most popular, systematic process to database solutions. Which Azure SQL database is also referred to as RDBMS (Relational Database Management Systems). The data are structured into tables or associations. The Atlas Mongodb NoSQL database is called a non-relational database management systems. The data are included in unstructured tables or associations. In this research, evaluate both the Azure SQL and Atlas Mongodb NoSQL databases. During the experiment compare the loading time, response time, and retrieval time of both Azure SQL and Atlas Mongodb NoSQL databases, and justify which one is fast, efficient and better performance.

scholarly journals Information System databases for Neuropsychology Tests: case study in Boston Naming Test

SISFORMA ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 28
Author(s):  
Shinta Estri Wahyuningrum ◽  
Augustina Sulastri ◽  
Ridwan Sanjaya
Keyword(s):  
Information System ◽  
Relational Database ◽  
Neuropsychological Test ◽  
Database Design ◽  
Boston Naming Test ◽  
Specific Data ◽  
Nosql Database ◽  
Sample Data ◽  
Naming Test

In the field of psychology, determining the psychological condition of a person’s can be done using various types of tests. Neuropsychology test is a battery test that means every person should be taken 11 test in a moment. Each test has a different objective, as an example, The Boston Naming test is used to measure a person's ability in the language domain. The data stored for each data in the Boston Naming Test (BNT) is around 130 fields. Each test has different specific data. This makes the data grow rapidly and requires a database design that can accommodate this need.There are many approaches can be done to store the database such a relational database and NoSQL database. When the data are stored using relational methods and amount of data are large, there can be a lack of time in both processing and tracking. This article proposes a system to store the result of the neuropsychological test using the NoSQL database approach with sample data in subtest BNT.

Security of NoSQL Database Against Intruders

2019 ◽  
Vol 13 (1) ◽  
pp. 5-12 ◽  
Author(s):  
Khaleel Ahmad ◽  
Mohammad Shoaib Alam ◽  
Nur Izura Udzir
Keyword(s):  
Big Data ◽  
Relational Database ◽  
Large Scale ◽  
Distributed Databases ◽  
Structural Data ◽  
Database Management System ◽  
Base System ◽  
Security Measures ◽  
Nosql Databases ◽  
Nosql Database

Background: The evolution of distributed web-based applications and cloud computing has brought about the demand to store a large amount of big data in distributed databases. Such efficient systems offer excessive availability and scalability to users. The new type of database resolves many new challenges especially in large-scale and high concurrency applications which are not present in the relational database. NoSQL refers to non-relational databases that are different from the Relational Database Management System. Objective: NoSQL has many features over traditional databases such as high scalability, distributed computing, lower cost, schema flexibility, semi or un-semi structural data and no complex relationship. Method: NoSQL databases are “BASE” Systems. The BASE (Basically Available, Soft state, Eventual consistency), formulates the CAP theorem the properties of which are used by BASE System. The distributed computer system cannot guarantee all of the following three properties at the same time that is consistency, availability and partition tolerance. Results: As progressively sharp big data is saved in NoSQL databases, it is essential to preserve higher security measures to ensure safe and trusted communication across the network. In this patent, we describe the security of NoSQL database against intruders which is growing rapidly. Conclusion: This patent also defines probably the most prominent NoSQL databases and describes their security aspects and problems.

scholarly journals Queryng data in NoSQL databases

2019 ◽  
Vol 7 (1) ◽  
pp. 257-270
Author(s):  
Andrea Babić ◽  
Danijela Jakšić ◽  
Patrizia Poščić
Keyword(s):  
Relational Database ◽  
Database Queries ◽  
Nosql Databases ◽  
Related Research ◽  
Nosql Database

The goal of this paper is to give an overview of fundamental concepts and types of NoSQL databases, to show some examples of database queries, some related research, and the implementation of those queries in an original practical example. The introduction is a brief representation and description of the NoSQL database. There are also several comparisons of NoSQL database with the relational database. The next chapter contains a review of the basic NoSQL databases and their prototypes. In each of the following subchapters, the types of NoSQL databases are described in more detail and various queries which can be performed over them are presented. In the last chapter there is also a practical example of querying one of these databases.

scholarly journals Automatic NoSQL to Relational Database Transformation with Dynamic Schema Mapping

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zain Aftab ◽  
Waheed Iqbal ◽  
Khaled Mohamad Almustafa ◽  
Faisal Bukhari ◽  
Muhammad Abdullah
Keyword(s):  
Relational Database ◽  
Business Intelligence ◽  
Relational Databases ◽  
State Of The Art ◽  
Schema Mapping ◽  
Excellent Performance ◽  
Nosql Databases ◽  
Nosql Database ◽  
Domain Expertise ◽  
Database Transformation

Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability. However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intelligence, analytics, decision making, and reporting. The existing methods of NoSQL to relational database transformation require manual schema mapping, which requires domain expertise and consumes noticeable time. Therefore, an efficient and automatic method is needed to transform an unstructured NoSQL database into a structured database. In this paper, we proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically. In our experimental evaluation, we used MongoDB as a NoSQL database, and MySQL and PostgreSQL as relational databases to perform transformation tasks for different dataset sizes. We observed excellent performance, compared to the existing state-of-the-art methods, in transforming data from a NoSQL database into a relational database.

scholarly journals An Approach for Schema Extraction of NoSQL Columnar Databases: the HBase Case Study

2021 ◽  
Vol 12 (5) ◽  
Author(s):  
Angelo Augusto Frozza ◽  
Eduardo Dias Defreyn ◽  
Ronaldo Dos Santos Mello
Keyword(s):  
A Priori ◽  
Database System ◽  
Data Representation ◽  
Data Types ◽  
Data Interoperability ◽  
Nosql Databases ◽  
Prototype Tool ◽  
Nosql Database ◽  
Integration Data

Although NoSQL databases do not require a schema a priori, being aware of the database schema is essential for activities like data integration, data validation, or data interoperability. This paper presents a process for the extraction of columnar NoSQL database schemas. We adopt JSON as a canonical format for data representation, and we validate the proposed process through a prototype tool that is able to extract schemas from the HBase columnar NoSQL database system. HBase was chosen as a case study because it is one of the most popular columnar NoSQL solutions. When compared to related work, we innovate by proposing a simple solution for the inference of column data types for columnar NoSQL databases that store only byte arrays as column values, and a resulting schema that follows the JSON Schema format.

scholarly journals An Introduction of NoSQL Databases Based on Their Categories and Application Industries

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 106 ◽  
Author(s):  
Jeang-Kuo Chen ◽  
Wei-Zhe Lee
Keyword(s):  
Big Data ◽  
Relational Database ◽  
Data Access ◽  
Nosql Databases ◽  
Nosql Database

The popularization of big data makes the enterprise need to store more and more data. The data in the enterprise’s database must be accessed as fast as possible, but the Relational Database (RDB) has the speed limitation due to the join operation. Many enterprises have changed to use a NoSQL database, which can meet the requirement of fast data access. However, there are more than hundreds of NoSQL databases. It is important to select a suitable NoSQL database for a certain enterprise because this decision will affect the performance of the enterprise operations. In this paper, fifteen categories of NoSQL databases will be introduced to find out the characteristics of every category. Some principles and examples are proposed to choose an appropriate NoSQL database for different industries.

scholarly journals A Process for Inference of Columnar NoSQL Database Schemas

2020 ◽  
Author(s):  
Angelo Augusto Frozza ◽  
Eduardo Dias Defreyn ◽  
Ronaldo Dos Santos Mello
Keyword(s):  
A Priori ◽  
Database System ◽  
Data Validation ◽  
Data Types ◽  
Data Interoperability ◽  
Nosql Databases ◽  
Prototype Tool ◽  
Nosql Database ◽  
Integration Data

Although NoSQL Databases do not require a schema a priori, to be aware of the database schema is essential for activities like data integration, data validation or data interoperability. This paper presents a process for inference of columnar NoSQL DB schemas. We validate the proposed process through a prototype tool that is able to extract schemas from the HBase columnar NoSQL database system. HBase was chosen as a case study because it is one of the most popular columnar NoSQL solutions. When compared to related work, we novel by proposing a simple solution for the inference of column data types for columnar NoSQL databases that store only byte arrays as column values, as well as a generated schema that follows the JSON Schema format.

scholarly journals CASE STUDY OF TRADITIONAL RDBMS AND NOSQL DATABASE SYSTEM

2019 ◽  
Vol 7 (7) ◽  
pp. 351-359
Author(s):  
Yashraj Sharma ◽  
Yashasvi Sharma
Keyword(s):  
Relational Database ◽  
System Architecture ◽  
Relational Databases ◽  
Database System ◽  
Database Management System ◽  
Nosql Databases ◽  
Advantages And Disadvantages ◽  
Relational Database System ◽  
Big Data Applications ◽  
Nosql Database

On the basis of reliability, rational models are useful but not in terms of systems which involve huge amount of data; in such cases, non-relational models are much more useful. To store large chunks of data, NoSQL databases are used. NoSQL databases are scalable and wide ranged because they are non-relationally distributed. In relational databases, it was not possible to manage data which involved very large number of Big Data applications hence the concept of NoSQL database was introduced. There are a lot of advantages of NoSQL which not only involve its own features but also some features of relational database management system. The severe benefit of NoSQL database is that it is an open source system which helps to adapt many numbers of features for newly generated applications. This paper is focused on understanding the concepts of non-relational database system architecture with relational database system architecture and figure out the advantages and disadvantages of both simultaneously.

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