A Relational Database Information System for a Small Clinical Laboratory

1989 ◽  
Vol 20 (8) ◽  
pp. 572-576
Author(s):  
Tsai F. Wu ◽  
Emmet J. Lamb
Keyword(s):  
Information System ◽  
Relational Database ◽  
Clinical Laboratory

Current Status of a Medical Information System

1970 ◽  
Vol 09 (03) ◽  
pp. 149-160 ◽  
Author(s):  
E. Van Brunt ◽  
L. S. Davis ◽  
J. F. Terdiman ◽  
S. Singer ◽  
E. Besag ◽  
...  
Keyword(s):  
Information System ◽  
Medical Records ◽  
Medical Information ◽  
Clinical Laboratory ◽  
Development Program ◽  
Medical Data ◽  
Direct Access ◽  
Medical Information System ◽  
Current Status ◽  
Central System

A pilot medical information system is being implemented and currently is providing services for limited categories of patient data. In one year, physicians’ diagnoses for 500,000 office visits, 300,000 drug prescriptions for outpatients, one million clinical laboratory tests, and 60,000 multiphasic screening examinations are being stored in and retrieved from integrated, direct access, patient computer medical records.This medical information system is a part of a long-term research and development program. Its major objective is the development of a multifacility computer-based system which will support eventually the medical data requirements of a population of one million persons and one thousand physicians. The strategy employed provides for modular development. The central system, the computer-stored medical records which are therein maintained, and a satellite pilot medical data system in one medical facility are described.

scholarly journals A generator of SQL schema specifications

2007 ◽  
Vol 4 (2) ◽  
pp. 81-100 ◽  
Author(s):  
Slavica Aleksic ◽  
Ivan Lukovic ◽  
Pavle Mogin ◽  
Miro Govedarica
Keyword(s):  
Information System ◽  
Normal Form ◽  
Relational Database ◽  
Sql Server ◽  
Database Schema ◽  
Intelligent Support ◽  
Case Tool ◽  
Design Environments ◽  
Data Constraints

IIS*Case is an integrated CASE tool that supports the automation and intelligent support of complex and highly formalized design and programming tasks in the development of an information system. IIS*Case, as a tool from the class of domain oriented design environments, generates relational database schemas in 3rd normal form with all relevant data constraints. SQL Generator is an IIS*Case tool that generates the implementation specification of a database schema according to ANSI SQL:2003 standard. The generator may also produce a database schema specification for Microsoft SQL Server or Oracle DBMSs. The paper describes SQL Generator's traits, considers aspects of its application, and shows its use in the implementation of a complex database constraint using procedural mechanisms of a particular relational DBMS. SQL Generator is implemented in Java and Oracle JDeveloper environment.

scholarly journals Using ontology merging for the integration of information systems and the production capacity planning system

2019 ◽  
pp. 401-408
Author(s):  
N Yarushkina ◽  
A Romanov ◽  
A Filippov ◽  
A Dolganovskaya ◽  
M Grigoricheva
Keyword(s):  
Information System ◽  
Information Systems ◽  
Relational Database ◽  
Data Model ◽  
Capacity Planning ◽  
Production Capacity ◽  
Semantic Integration ◽  
Data Sources ◽  
Planning System ◽  
Ontology Merging

This article describes the method of integrating information systems of an aircraft factory with the production capacity planning system based on the ontology merging. The ontological representation is formed for each relational database (RDB) of integrated information systems. The ontological representation is formed in the process of analyzing the structure of the relational database of the information system (IS). Based on the ontological representations merging the integrating data model is formed. The integrating data model is a mechanism for semantic integration of data sources.

The development of a third generation system for entering microbiology data into a clinical laboratory information system

1988 ◽  
Vol 12 (6) ◽  
pp. 365-382 ◽  
Author(s):  
Arthur A. Eggert ◽  
Kenneth A. Emmerich ◽  
Carol A. Spiegel ◽  
Gary J. Smulka ◽  
Patricia A. Horstmeier ◽  
...  
Keyword(s):  
Information System ◽  
Clinical Laboratory ◽  
Third Generation ◽  
Laboratory Information System ◽  
Generation System

Efficiency of JSON approach for Data Extraction and Query Retrieval

2016 ◽  
Vol 4 (1) ◽  
pp. 203 ◽  
Author(s):  
Mohd Kamir Yusof ◽  
Mustafa Man
Keyword(s):  
Information System ◽  
Relational Database ◽  
Execution Time ◽  
Database Management ◽  
Data Extraction ◽  
Extensible Markup Language ◽  
Markup Language ◽  
Huge Data ◽  
Extensible Markup ◽  
Database Technology

<p>Students’ Information System (SIS) in Universiti Sultan Zainal Abidin (UniSZA) handles thousands of records on the information of students, subject registration, etc. Efficiency of storage and query retrieval of these records is the matter of database management especially involving with huge data. However, the execution time for storing and retrieving these data are still considerably inefficient due to several factors. In this contribution, two database approaches namely Extensible Markup Language (XML) and JavaScript Object Notation (JSON) were investigated to evaluate their suitability for handling thousands records in SIS. The results showed JSON is the best choice for storage and query speed. These are essential to cope with the characteristics of students’ data. Whilst, XML and JSON technologies are relatively new to date in comparison to the relational database. Indeed, JSON technology demonstrates greater potential to become a key database technology for handling huge data due to an increase of data annually.</p>

scholarly journals Effects of system tuning and RAM disk on the performance of a clinical laboratory information system

1989 ◽  
Vol 11 (3) ◽  
pp. 119-123 ◽  
Author(s):  
Arthur A. Eggert ◽  
Kenneth A. Emmerich ◽  
Thomas J. Blankenheim ◽  
Gary J. Smulka
Keyword(s):  
Operating System ◽  
Information System ◽  
Computer System ◽  
Clinical Laboratory ◽  
Storage Device ◽  
Laboratory Information System ◽  
Laboratory Computer ◽  
Before And After

Improvements in the performance of a laboratory computer system do not necessarily require the replacement of major portions of the system and may not require the acquisition of any hardware at all. Major bottlenecks may exist in the ways that the operating system manages its resources and the algorithm used for timesharing decisions. Moreover, significant throughput improvements may be attainable by switching to a faster storage device if substantial disk activity is performed. In this study the fractions of time used for each of the types of tasks a laboratory computer system performs (e.g. applications programs, disk transfer, queue cycler) are defined and measured. Methods for reducing the time fractions of the various types of overhead are evaluated by doing before and after studies. The combined results of the three studies indicated that a 50% improvement could be gained through system tuning and faster storage without replacement of the computer itself

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