scholarly journals Improving the performance of big data databases

2019 ◽  
Vol 4 (2) ◽  
pp. 206-220
Author(s):  
Dashne Raouf Arif ◽  
Nzar Abdulqadir Ali
Keyword(s):  
Relational Database ◽  
Relational Databases ◽  
Query Language ◽  
Database Management System ◽  
Data Sets ◽  
Relational Database Management System ◽  
Nosql Database ◽  
Sql Database ◽  
Performance Results ◽  
Relational Database Management

Real-time monitoring systems utilize two types of database, they are relational databases such as MySQL and non-relational databases such as MongoDB. A relational database management system (RDBMS) stores data in a structured format using rows and columns. It is relational because the values of the tables are connected. A non-relational database is a database that does not adopt the relational structure given by traditional. In recent years, this class of databases has also been referred to as Not only SQL (NoSQL).  This paper discusses many comparisons that have been conducted on the execution time performance of types of databases (SQL and NoSQL). In SQL (Structured Query Language) databases different algorithms are used for inserting and updating data, such as indexing, bulk insert and multiple updating. However, in NoSQL different algorithms are used for inserting and updating operations such as default-indexing, batch insert, multiple updating and pipeline aggregation. As a result, firstly compared with related papers, this paper shows that the performance of both SQL and NoSQL can be improved. Secondly, performance can be dramatically improved for inserting and updating operations in the NoSQL database compared to the SQL database. To demonstrate the performance of the different algorithms for entering and updating data in SQL and NoSQL, this paper focuses on a different number of data sets and different performance results. The SQL part of the paper is conducted on 50,000 records to 3,000,000 records, while the NoSQL part of the paper is conducted on 50,000 to 16,000,000 documents (2GB) for NoSQL. In SQL, three million records are inserted within 606.53 seconds, while in NoSQL this number of documents is inserted within 67.87 seconds. For updating data, in SQL 300,000 records are updated within 271.17 seconds, while for NoSQL this number of documents is updated within just 46.02 seconds.  

scholarly journals PENGERIAN DAN KOMPONEN SQL MUHAMMAD DENNY PRAYOGA 165100046

2018 ◽  
Author(s):  
Muhammad denny prayoga
Keyword(s):  
Relational Database ◽  
Management System ◽  
Database Management ◽  
Query Language ◽  
Database Management System ◽  
Relational Database Management System ◽  
Structured Query Language ◽  
Relational Database Management

Pengertian SQLSQL (Structured Query Language) adalah sebuah bahasa yang digunakan untuk mengakses data dalam basis data relasional.SQL secara de facto merupakan bahasa standar yang digunakan dalam RDBMS (relational database management system).Saat ini hampir semua server basis data yang ada mendukung bahasa SQL untuk melakukan manajemen datanya.SQL merupakan bahasa baku (ANSI/SQL), non prosedural dan berorientasi himpunan (set oriented language)SQL dapat digunakan baik secara interaktif atau ditempelkan (embedded) pada sebuah program aplikasi.

scholarly journals PENGERTIAN DAN KOMPONEN SQL MUHAMMAD DENNY PRAYOGA 165100046

2018 ◽  
Author(s):  
Muhammad denny prayoga
Keyword(s):  
Relational Database ◽  
Management System ◽  
Database Management ◽  
Query Language ◽  
Database Management System ◽  
Relational Database Management System ◽  
Structured Query Language ◽  
Relational Database Management

Pengertian SQLSQL (Structured Query Language) adalah sebuah bahasa yang digunakan untuk mengakses data dalam basis data relasional.SQL secara de facto merupakan bahasa standar yang digunakan dalam RDBMS (relational database management system).Saat ini hampir semua server basis data yang ada mendukung bahasa SQL untuk melakukan manajemen datanya.SQL merupakan bahasa baku (ANSI/SQL), non prosedural dan berorientasi himpunan (set oriented language)SQL dapat digunakan baik secara interaktif atau ditempelkan (embedded) pada sebuah program aplikasi.

Towards a Fuzzy Object-Relational Database Model

2011 ◽  
pp. 435-461 ◽  
Author(s):  
Carlos D. Barranco ◽  
Jesús R. Campaña ◽  
Juan M. Medina
Keyword(s):  
Relational Database ◽  
Management System ◽  
Database Management ◽  
Relational Databases ◽  
Database Management System ◽  
Data Types ◽  
Relational Database Management System ◽  
Database Model ◽  
Object Relational ◽  
Relational Database Management

This chapter introduces a fuzzy object-relational database model including fuzzy extensions of the basic object-relational databases constructs, the user-defined data types, and the collection types. The fuzzy extensions of these constructs focus on two main flexible aspects, a way to flexibly compare complex data types and an extension of collection types allowing partial membership of its elements. Collection operators are also adapted to consider flexibly comparable domains for its elements. Such a fuzzy object-relational database model, and its implementation in a fuzzy object-relational database management system, provides an easy and effective way to manage a great amount of complex fuzzy data in object-relational databases for emerging fuzzy applications. As a sample of the proposal advantages, an application for dominant color based image retrieval, which is built on an object-relational database management system implementing the proposed fuzzy database model, is introduced.

Relational Database Management System

2021 ◽  
pp. 47-78
Author(s):  
Jagdish Chandra Patni ◽  
Hitesh Kumar Sharma ◽  
Ravi Tomar ◽  
Avita Katal
Keyword(s):  
Relational Database ◽  
Management System ◽  
Database Management ◽  
Database Management System ◽  
Relational Database Management System ◽  
Relational Database Management

Automating Quality Oncology Practice Initiative Quality Measure NHL78a

2017 ◽  
pp. 1-6
Author(s):  
Richard Mansour ◽  
Samip Master
Keyword(s):  
Hepatitis B ◽  
Relational Database ◽  
Management System ◽  
Database Management ◽  
Quality Measurement ◽  
Database Management System ◽  
Quality Measure ◽  
Relational Database Management System ◽  
Oncology Practice ◽  
Relational Database Management

Purpose Quality measurement and improvement is a focus of ASCO. In the era of electronic health records (EHRs), computerized order entry, and medication administration records, quality monitoring can be an automated process. The EHR data are usually stored within tables in a relational database management system. ASCO Quality Oncology Practice Initiative measure NHL78a (hepatitis B virus antigen test and hepatitis B core antibody test within 3 months before initiation of obinutuzumab, ofatumumab, or rituximab for patients with non-Hodgkin lymphoma) presents an opportunity for automation of a quality measure using existing data in the EHR. Methods We used a locally developed Structured Query Language (SQL) language procedure in the Microsoft SQL Query Manager to access the EPIC CLARITY database. Access to the relational database management system of the EHR permits rapid case identification (the denominator set) of the unique ID of all of the patients who have received one of the target medications (ie, obinutuzumab, ofatumumab, or rituximab). Then, we went through a six-step process to find the number of patients who passed or failed the quality measure. Results When the final SQL procedure executes, it takes < 5 seconds to see the result set for a 12-month period. The procedure can be changed to incorporate a desired date range. Once the SQL procedure is created, there is essentially no labor and low costs to run the procedure at specific time intervals. Conclusion Our method of quality measurement using EHRs is cost effective, fast, and precise, and can be reproduced at other centers.

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