scholarly journals A Comparative study of SQL Databases and NoSQL Databases for E-Commerce

2021 ◽  
Vol 9 (12) ◽  
pp. 409-412
Author(s):  
Disha Nakhare
Keyword(s):  
Comparative Study ◽  
Relational Databases ◽  
Query Language ◽  
Economic Status ◽  
Web Engineering ◽  
Nosql Databases ◽  
Structured Query Language

Abstract: With the advent of E-Commerce, businesses persistently examine various ways to improvise and accomplish their demands with web engineering that provide notable resolution. The progress in economic status demands colossal databases that store the data efficiently. The databases currently used are relational or non-relational. Both these types have their benefits and limitations that influence the overall processing of data. Non-relational databases are referred to as NoSQL-not only SQL, and Relational databases are known as SQL-Structured Query Language. It has been suggested in many studies that NoSQL databases surpass SQL databases. Our paper aims to evaluate these claims by analyzing the CRUD [Create, Read, Update, Delete] operations executed by both database types. Keywords: NoSQL, SQL, Non-relational Databases, MySQL, E-Commerce, MongoDb , Relational Databases

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>

Query Languages for Graph Databases

2019 ◽  
pp. 645-659
Author(s):  
Kornelije Rabuzin
Keyword(s):  
Relational Databases ◽  
Query Language ◽  
Main Idea ◽  
Query Languages ◽  
End Users ◽  
The Other ◽  
Graph Database ◽  
Graph Databases ◽  
Nosql Databases ◽  
The Past

In the past few years, many NoSQL databases have emerged, including graph databases. NoSQL databases have certain advantages and they can be used in certain domains as an alternative to relational databases. In order to use graph databases, one needs to be familiar with specific languages like Cypher Query Language (CQL) or Gremlin. However, some statements in CQL can be considered too complex for end users as it is shown later on. Because of that, the main idea of this chapter is to explore two other languages for graph databases. One of them is new and it is used to pose queries visually. Since CQL does not support recursion, views, etc., the other language is used to show how to use recursion and views on a graph database.

Formalising PFSQL queries using ŁΠ fuzzy logic

2011 ◽  
Vol 22 (3) ◽  
pp. 533-547 ◽  
Author(s):  
ALEKSANDAR PEROVIĆ ◽  
ALEKSANDAR TAKAČI ◽  
SRDJAN ŠKRBIĆ
Keyword(s):  
Fuzzy Logic ◽  
Constraint Satisfaction ◽  
Relational Databases ◽  
Query Language ◽  
Constraint Satisfaction Problem ◽  
Current Paper ◽  
Conservative Extension ◽  
Logic Formula ◽  
Structured Query Language

Using the concept of a generalised priority constraint satisfaction problem, we previously found a way to introduce priority queries into fuzzy relational databases. The results were PFSQL (Priority Fuzzy Structured Query Language) together with a database independent interpreter for it. In an effort to improve the performance of the resolution of PFSQL queries, the aim of the current paper is to formalise PFSQL queries by obtaining their interpretation in an existing fuzzy logic. We have found that the ŁΠ logic provides sufficient elements. The SELECT line of PFSQL queries is semantically a formula of some fuzzy logic, and we show that such formulas can be naturally expressed in a conservative extension of the ŁΠ logic. Furthermore, we prove a theorem that gives the PSPACE containment for the complexity of finding a model for a given ŁΠ logic formula.

scholarly journals Fuzzy improvement of the SQL

2011 ◽  
Vol 21 (2) ◽  
pp. 239-251 ◽  
Author(s):  
Miroslav Hudec
Keyword(s):  
Selection Criteria ◽  
Relational Databases ◽  
Query Language ◽  
Linguistic Terms ◽  
Proposed Model ◽  
Logical Condition ◽  
Structured Query Language ◽  
The Given

Structured Query Language (SQL) is used to obtain data from relational databases. Fuzzy improvement of SQL queries has advantages in cases when the user cannot unambiguously define selection criteria or when the user wants to examine data that almost meet the given criteria. In this paper we examine a realization of the fuzzy querying concept. For this purposes the fuzzy generalized logical condition for the WHERE part of the SQL is created. It allows users to create queries by linguistic terms. The proposed model is an extension of the SQL so that no modification inside databases has to be undertaken.

Query Languages for Graph Databases

2018 ◽  
pp. 2031-2042
Author(s):  
Kornelije Rabuzin
Keyword(s):  
Relational Databases ◽  
Query Language ◽  
Main Idea ◽  
Query Languages ◽  
End Users ◽  
The Other ◽  
Graph Database ◽  
Graph Databases ◽  
Nosql Databases ◽  
The Past

In the past few years many NoSQL databases have emerged, including graph databases. NoSQL databases have certain advantages and they can be used in certain domains as an alternative to relational databases. In order to use graph databases, one needs to be familiar with specific languages like Cypher Query Language (CQL) or Gremlin. However, some statements in CQL can be considered too complex for end users as it is shown later on. Because of that the main idea of this paper is to explore two other languages for graph databases. One of them is new and it is used to pose queries visually. Since CQL does not support recursion, views, etc., the other language is used to show how to use recursion and views on a graph database.

The End of Relational Databases Domination

2018 ◽  
pp. 1-33
Keyword(s):  
Relational Databases ◽  
Query Language ◽  
Nosql Databases ◽  
Database Development ◽  
Relational Data Model ◽  
Durability Properties ◽  
Nosql Database ◽  
Soft State ◽  
Database Models ◽  
Consistent Approach

The chapter gives an overview of the three main stages of database development: hierarchical and network database models, relational database model, and NoSQL databases. It gives a short overview of the pillars of relational databases: relational data model, ACID (atomicity, consistency, isolation, and durability) properties of a transaction, and SQL (structured query language). Also, the concepts that make the base for NoSQL database development are explained, including the CAP (Consistency, Availability, Partitioning) theorem, the BASE (Basically Available, Soft-state, Eventually consistent) approach, and the sharding phenomenon. At last, the limitations of relational databases which led to the development of NoSQL databases are discussed.

scholarly journals A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube

AI ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 720-737
Author(s):  
Fadi H. Hazboun ◽  
Majdi Owda ◽  
Amani Yousef Owda
Keyword(s):  
Natural Language ◽  
Data Warehouse ◽  
Language Processing ◽  
Relational Databases ◽  
Query Language ◽  
Response Speed ◽  
System Response ◽  
Complex Query ◽  
Structured Query Language ◽  
Self Service

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database

2021 ◽  
Vol 13 (3) ◽  
pp. 38-63
Author(s):  
Houcine Matallah ◽  
Ghalem Belalem ◽  
Karim Bouamrane
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Comparative Study ◽  
Electronic Commerce ◽  
Relational Database ◽  
Relational Databases ◽  
Database Management System ◽  
Nosql Databases ◽  
Nosql Database ◽  
Change Of Scale

NoSQL databases are new architectures developed to remedy the various weaknesses that have affected relational databases in highly distributed systems such as cloud computing, social networks, electronic commerce. Several companies loyal to traditional relational SQL databases for several decades seek to switch to the new “NoSQL” databases to meet the new requirements related to the change of scale in data volumetry, the load increases, the diversity of types of data handled, and geographic distribution. This paper develops a comparative study in which the authors will evaluate the performance of two databases very widespread in the field: MySQL as a relational database and MongoDB as a NoSQL database. To accomplish this confrontation, this research uses the Yahoo! Cloud Serving Benchmark (YCSB). This contribution is to provide some answers to choose the appropriate database management system for the type of data used and the type of processing performed on that data.

scholarly journals Research of NOSQL and SQL and Designing a Better Database Schema for Databases

2019 ◽  
Vol 8 (6S) ◽  
pp. 177-182
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Query Language ◽  
Storage System ◽  
Database Schema ◽  
Nosql Databases ◽  
Storage Security ◽  
File Storage ◽  
Structured Query Language ◽  
Day By Day

The amount of data storage and querying is increasing day by day. The requirement of data-storage, security, scalability and management is an alarming issue. Structured Query Language (SQL) databases was designed to overcome most of the trouble faced over tradition file storage system. But the alarming rate of data storage leads to Not only Structured Query Language (NoSQL) databases. in this paper, various aspects of SQL and NoSQL are compared with respect to their data management and to develop a better schema for data management is attempted

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