MonogDB

2017 ◽  
pp. 64-91
Author(s):  
Sonali Tidke
Keyword(s):  
Management System ◽  
Database Management ◽  
Query Language ◽  
Database Management System ◽  
Management Model ◽  
Nosql Databases ◽  
Performance Indexes ◽  
And Behavior ◽  
Relational Database Management ◽  
Partitioning Technique

MongoDB is a NoSQL type of database management system which does not adhere to the commonly used relational database management model. MongoDB is used for horizontal scaling across a large number of servers which may have tens, hundreds or even thousands of servers. This horizontal scaling is performed using sharding. Sharding is a database partitioning technique which partitions large database into smaller parts which are easy to manage and faster to access. There are hundreds of NoSQL databases available in the market. But each NoSQL product is different in terms of features, implementations and behavior. NoSQL and RDBMS solve different set of problems and have different requirements. MongoDB has a powerful query language which extends SQL to JSON enabling developers to take benefit of power of SQL and flexibility of JSON. Along with support for select/from/where type of queries, MongoDB supports aggregation, sorting, joins as well as nested array and collections. To improve query performance, indexes and many more features are also available.

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.

The Impact of Big Data on Security

Big Data ◽  
2016 ◽  
pp. 1495-1518
Author(s):  
Mohammad Alaa Hussain Al-Hamami
Keyword(s):  
Social Media ◽  
Big Data ◽  
Management System ◽  
Database Management ◽  
Database Systems ◽  
Structured Data ◽  
Database Management System ◽  
Unstructured Data ◽  
And Behavior ◽  
The Impact

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.

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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