Database Design for Archives Management System

2013 ◽  
Vol 385-386 ◽  
pp. 1734-1737
Author(s):  
Kai He You ◽  
Hong Yan Li ◽  
Sheng Zhao
Keyword(s):  
Relational Database ◽  
Management System ◽  
Database Design ◽  
Database Management System ◽  
Security Requirements ◽  
Functional Requirements ◽  
Modern Information Technology ◽  
The Core ◽  
Diagram Logic ◽  
Business Requirements

Archive management system based on modern information technology as the support, the archives information as management object, the archives work as the core and implement management information system, database design is the core work of the development of archives management system work, this article launches the research from four aspects according to the database design process. First, from the business requirements, functional requirements, performance requirements, such as security requirements for demand analysis; Second, according to the result of requirement analysis, conceptual design using E-R diagram; Third, converting E-R diagram logic structure supported by the database management system; Fourth, for the logic model to determine the storage structure and access method. In this paper, it based on the relational database design, a relational database with full-text database is the development direction of the future.

Research on Framework Structure for Scientific Research Management System

2013 ◽  
Vol 385-386 ◽  
pp. 1776-1779 ◽  
Author(s):  
Kai He You ◽  
Li Jun Li ◽  
Mei Zhi Zhao
Keyword(s):  
Relational Database ◽  
Management System ◽  
Database Design ◽  
Database Management System ◽  
Research Management ◽  
Security Requirements ◽  
Functional Requirements ◽  
Modern Information Technology ◽  
The Core ◽  
Diagram Logic

Archive management system based on modern information technology as the support, the archives information as management object, the archives work as the core and implement management information system, database design is the core work of the development of archives management system work, this article launches the research from four aspects according to the database design process. First, from the business requirements, functional requirements, performance requirements, such as security requirements for demand analysis; Second, according to the result of requirement analysis, conceptual design using E-R diagram; Third, converting E-R diagram logic structure supported by the database management system; Fourth, for the logic model to determine the storage structure and access method. In this paper, it based on the relational database design, a relational database with full-text database is the development direction of the future.

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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