scholarly journals A comparison study of NoSQL document-oriented database system

2019 ◽  
Vol 8 (1) ◽  
pp. 27
Author(s):  
Sugimiyanto Suma ◽  
Fahad Alqurashi
Keyword(s):  
Execution Time ◽  
Storage System ◽  
Data Generation ◽  
Fast Analysis ◽  
Local Cluster ◽  
Nosql Databases ◽  
Nosql Database ◽  
Simple Program ◽  
System Developer ◽  
Database Engine

By increasing data generation at these day, requirement for a sufficient storage system are strongly needed by stakeholders to store and access huge number of data in efficient way for fast analysis and decision. While RDBMS cannot deal with this challenge, NoSQL has emerged as a solution to address this challenge. There have been plenty of NoSQL database engine with their categories and characteristics, especially for document-oriented database. However, it makes a confusion for the system developer to choose the appropriate NoSQL database for their system. This paper is our preliminary report to provide a comparison of NoSQL databases. The comparison is based on performance of execution time which is measured by building a simple program. This experiment was done in our local cluster by exploiting around 1 million datasets. The result shows that RDB has better performance than CDB in terms of execution time.

Big Data

2017 ◽  
pp. 158-179
Author(s):  
Vinod Kumar ◽  
Ramjeevan Singh Thakur
Keyword(s):  
Big Data ◽  
Database System ◽  
Smart Phones ◽  
Data Handling ◽  
Data Generation ◽  
Business Organizations ◽  
Nosql Databases ◽  
Data Formats ◽  
Nosql Database ◽  
Tools And Techniques

With every passing day, data generation is increasing exponentially, its volume, variety, velocity are making it quite challenging to analyze, interpret, visualize for gaining the greater insights from the available data. Billions of networked sensors are being embedded in devices such as smart phones, automobiles, social media sites, laptop, PC's and industrial machines etc. that operates, generate and communicate data. Thus, the data obtained from various resources exists in structured, semi-structured and unstructured form. The traditional database system is not suitable to handle these data formats. Therefore, new tools and techniques are developed to work with these data. NoSQL is one of them. Currently, many NoSQL database are available in the market, each one of them specially designed to solve specific type of data handling problems, most of the NoSQL databases are developed with special attention to problem of business organizations and enterprises. The chapter focuses various aspects of NoSQL as tool for handling the big data.

scholarly journals Performance Benchmarking of Key-Value Store NoSQL Databases

2018 ◽  
Vol 8 (6) ◽  
pp. 5333
Author(s):  
Omoruyi Osemwegie ◽  
Kennedy Okokpujie ◽  
Nsikan Nkordeh ◽  
Charles Ndujiuba ◽  
Samuel John ◽  
...  
Keyword(s):  
Data Storage ◽  
Web Applications ◽  
Query Language ◽  
Research Work ◽  
Database Systems ◽  
Nosql Databases ◽  
Performance Benchmarking ◽  
Nosql Database ◽  
Structured Query Language ◽  
Web Developers

<p>Increasing requirements for scalability and elasticity of data storage for web applications has made Not Structured Query Language NoSQL databases more invaluable to web developers. One of such NoSQL Database solutions is Redis. A budding alternative to Redis database is the SSDB database, which is also a key-value store but is disk-based. The aim of this research work is to benchmark both databases (Redis and SSDB) using the Yahoo Cloud Serving Benchmark (YCSB). YCSB is a platform that has been used to compare and benchmark similar NoSQL database systems. Both databases were given variable workloads to identify the throughput of all given operations. The results obtained shows that SSDB gives a better throughput for majority of operations to Redis’s performance.</p>

How NoSQL Databases Work

2018 ◽  
pp. 124-175
Keyword(s):  
Distributed Storage ◽  
Practical Implementation ◽  
Query Execution ◽  
Nosql Databases ◽  
Consistency Model ◽  
Nosql Database ◽  
Distributed Cluster ◽  
Innovative Approaches

The chapter explains how NoSQL databases work. Since different NoSQL databases are classified into four categories (key-value, column-family, document, and graph stores), three main features of NoSQL databases are chosen, and their practical implementation is explained using examples of one or two typical NoSQL databases from each NoSQL database category. The three chosen features are: distributed storage architecture that comprises the distributed, cluster-oriented, and horizontally scalable features; consistency model that refers to the CAP and BASE features; query execution that refers to the schemaless feature. These features are chosen because, through them, it is possible to describe most of the new and innovative approaches that NoSQL databases bring to the database world.

NoSQL Data Modeling

2018 ◽  
pp. 94-123
Keyword(s):  
Conceptual Model ◽  
Data Modeling ◽  
Basic Structure ◽  
Object Modeling ◽  
Similar Model ◽  
Nosql Databases ◽  
Nosql Database ◽  
Er Model ◽  
Entity Relationship ◽  
Huge Challenge

The chapter discusses the necessity for data modeling in NoSQL world. The NoSQL data modeling is a huge challenge because one of the main features of NoSQL databases is that they are schema-free, that is they allow data manipulation without the need for the previous modeling or developing an entity-relationship (ER) or similar model. Although the absence of a schema can be an advantage in some situations, with the increase in the number of NoSQL database implementations, it appears that the absence of a conceptual model can be a source of substantial problems. In order to better understand the need for data modeling in NoSQL databases, first the basic structure of an ER model and an analysis of its limitations are summarized, especially regarding an application in NoSQL databases. The concept and Object modeling notation is presented as one of the possible solutions for data modeling in NoSQL databases.

Research of Load Balancing Based on NOSQL Database

2014 ◽  
Vol 602-605 ◽  
pp. 3371-3374
Author(s):  
Peng Wang ◽  
Yan Qi
Keyword(s):  
Load Balancing ◽  
Distributed Storage ◽  
Storage System ◽  
Sql Server ◽  
Distributed Storage System ◽  
Nosql Database ◽  
Consistent Hashing ◽  
Cache Hit Ratio ◽  
Hashing Algorithm ◽  
Balancing Principle

The NOSQL database to support data and high concurrent read and write,scalability and high availability features in a distributed storage system which has been applied widely. In this paper, through the research of load balancing in distributed storage system,and it proposes the consistent hashing algorithm and the virtual node strategy, in order to improve the load balancing of the system and increase the cache hit ratio. For the load balancing principle of NOSQL and SQL Server, analysis and comparison of the experimental data.The result shows that, with the increase of the number of virtual nodes, the cache hit ratio of NOSQL is higher than the cache hit ratio of SQL Server.

scholarly journals A Framework for Executing Complex Querying for Relational and NoSQL Databases (CQNS)

2020 ◽  
Vol 4 (5) ◽  
Author(s):  
Eman A. Khashan ◽  
Ali I. El Desouky ◽  
Sally M. Elghamrawy
Keyword(s):  
Mixed Data ◽  
Query Execution ◽  
Data Systems ◽  
Nosql Databases ◽  
Complex Queries ◽  
Huge Data ◽  
Data Store ◽  
Nosql Database ◽  
Sql Query ◽  
Spark Framework

The increasing of data on the web poses major confrontations. The amount of stored data and query data sources have become needful features for huge data systems. There are a large number of platforms used to handle the NoSQL database model such as: Spark, H2O and Hadoop HDFS / MapReduce, which are suitable for controlling and managing the amount of big data. Developers of different applications impose data stores on difficult tasks by interacting with mixed data models through different APIs and queries. In this paper, a complex SQL Query and NoSQL (CQNS) framework that acts as an interpreter sends complex queries received from any data store to its corresponding executable engine called CQNS. The proposed framework supports application queries and database transformation at the same time, which in turn speeds up the process. Moreover, CQNS handles many NoSQL databases like MongoDB and Cassandra. This paper provides a spark framework that can handle SQL and NoSQL databases. This work also examines the importance of MongoDB block sharding and composition. Cassandra database deals with two types of sections vertex and edge Portioning. The four scenarios criteria datasets are used to evaluate the proposed CQNS to query the various NOSQL databases in terms of optimization performance and timing of query execution. The results show that among the comparative system, CQNS achieves optimum latency and productivity in less time.

scholarly journals IMPLEMENTASI WEBSITE PENCARIAN KOS DENGAN NoSQL

2017 ◽  
Vol 2 (2) ◽  
pp. 103
Author(s):  
Danny Kriestanto ◽  
Alif Benden Arnado
Keyword(s):  
Data Storage ◽  
Structure Data ◽  
Relational Databases ◽  
New Technology ◽  
Unstructured Data ◽  
Nosql Databases ◽  
Storage Technology ◽  
Nosql Database ◽  
Boarding House ◽  
New Type

The new technology of database has moved forward the relational databases. Now, the massive and unstructured data encourage experts to create a new type of database without using query. One of this technology is called NoSQL (Not Only SQL). One of the developing RDBMS that using this technique is MongoDB, which already supporting data storage technology that is no longer need for structured tables and rigid-typed of data. The schema was made flexible to handle the changes of data. The MongoDB data collecting characteristics in the form of arrays is considered suitable for the implementation of boarding house searching where each of the boarding houses have their own scenario structures. MongoDB also supports several programming language, including PHP with Bootstrap material as interface. The results of the research showed that there are alot of difference in implementing a NoSQL database with the regular relational one. NoSQL databases considered alot more complicated in structure, data type, even the CRUD system. The results also showed that in order to view an array inside another array will need two processes.

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.

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.

Towards Convergence in Information Systems Design

2020 ◽  
pp. 247-263
Author(s):  
Deepika Prakash
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Information Systems ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Systems Design ◽  
Information Model ◽  
Information Systems Design ◽  
Nosql Databases ◽  
Nosql Database

Three technologies—business intelligence, big data, and machine learning—developed independently and address different types of problems. Data warehouses have been used as systems for business intelligence, and NoSQL databases are used for big data. In this chapter, the authors explore the convergence of business intelligence and big data. Traditionally, a data warehouse is implemented on a ROLAP or MOLAP platform. Whereas MOLAP suffers from having propriety architecture, ROLAP suffers from the inherent disadvantages of RDBMS. In order to mitigate the drawbacks of ROLAP, the authors propose implementing a data warehouse on a NoSQL database. They choose Cassandra as their database. For this they start by identifying a generic information model that captures the requirements of the system to-be. They propose mapping rules that map the components of the information model to the Cassandra data model. They finally show a small implementation using an example.

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