scholarly journals Design of a Real-Time Facility Diagnosis & Complex Data Management System Using IT Convergence Technology

2014 ◽  
Vol 19 (5) ◽  
pp. 53-60
Author(s):  
Moon-Sik Kang
Keyword(s):  
Data Management ◽  
Real Time ◽  
Management System ◽  
Data Management System ◽  
Complex Data ◽  
It Convergence

Database Considerations for an ICU Patient Data Management System

1991 ◽  
Author(s):  
N. Fumai ◽  
C. Collet ◽  
M. Petroni ◽  
K. Roger ◽  
E. Saab ◽  
...  
Keyword(s):  
Data Management ◽  
Real Time ◽  
Data Storage ◽  
Relational Database ◽  
Management System ◽  
Patient Data ◽  
Patient Data Management System ◽  
Data Management System ◽  
Data Types ◽  
Management Structure

Abstract A Patient Data Management System (PDMS) is being developed for use in the Intensive Care Unit (ICU) of the Montreal Children’s Hospital. The PDMS acquires real-time patient data from a network of physiological bedside monitors and facilitates the review and interpretation of this data by presenting it as graphical trends, charts and plots on a color video display. Due to the large amounts of data involved, the data storage and data management processes are an important task of the PDMS. The data management structure must integrate varied data types and provide database support for different applications, while preserving the real-time acquisition of network data. This paper outlines a new data management structure which is based primarily on OS/2’s Extended Edition relational database. The relational database design is expected to solve the query shortcomings of the previous data management structure, as well as offer support for security and concurrency. The discussion will also highlight future advantages available from a network implementation.

Applications of Distributed Parallel Real Time Database on Power Grid

2013 ◽  
Vol 816-817 ◽  
pp. 488-492
Author(s):  
Li Xin Li ◽  
Wei Zhou ◽  
Qi Qiang Sun ◽  
Jiao Dai ◽  
Ji Zhong Han ◽  
...  
Keyword(s):  
Data Management ◽  
Real Time ◽  
Management System ◽  
Power Grid ◽  
Database Design ◽  
Experimental Results ◽  
Data Management System ◽  
Meta Data ◽  
The Real ◽  
Real Time Database

In order to make the real time database more suitable for the computing features, this article points to the distributed and parallel real time database design and architecture. First, a mapping table from table file to machine nodes is established, and then can use meta-data management system to store and manage the mapping table to meet the characteristics of high concurrent access. The whole network computation can access the unified interface provided by the real-time database, retrive data from each node, then collect the data. Experimental results show that this study and the systems designed can meet the computing requirements of a unified whole network.

scholarly journals Data Management System for a Semiautonomous Shuttle Car for Underground Room and Pillar Coal Mines

Automation ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 153-172
Author(s):  
Vasilis Androulakis ◽  
Steven Schafrik ◽  
Joseph Sottile ◽  
Zach Agioutantis
Keyword(s):  
Data Processing ◽  
Data Management ◽  
Real Time ◽  
Data Storage ◽  
Autonomous Navigation ◽  
Management System ◽  
Reliable Data ◽  
The Other ◽  
Data Management System ◽  
Shuttle Car

In recent years, autonomous solutions in the multidisciplinary field of mining engineering have been an extremely popular applied research topic. This is a result of the increasing demands of society on mineral resources along with the accelerating exploitation of the currently economically viable resources, which lead the mining sector to turn to deeper, more-difficult-to-mine orebodies. An appropriate data management system comprises a crucial aspect of the designing and the engineering of a system that involves autonomous or semiautonomous vehicles. The vast volume of data collected from onboard sensors, as well as from a potential IoT network dispersed around a smart mine, necessitates the development of a reliable data management strategy. Ideally, this strategy will allow for fast and asynchronous access to the data for real-time processing and decision-making purposes as well as for visualization through a corresponding human–machine interface. The proposed system has been developed for autonomous navigation of a coalmine shuttle car and has been implemented on a 1/6th scale shuttle car in a mock mine. It comprises three separate nodes, namely, a data collection node, a data management node, and a data processing and visualization node. This approach was dictated by the large amount of collected data and the need to ensure uninterrupted and fast data management and flow. The implementation of an SQL database server allows for asynchronous, real-time, and reliable data management, including data storage and retrieval. On the other hand, this approach introduces latencies between the data management node and the other two nodes. In general, these latencies include sensor latencies, network latencies, and processing latencies. However, the data processing and visualization module is able to retrieve and process the latest data and make a decision about the next optimal movement of the shuttle car prototype in less than 900 ms. This allows the prototype to navigate efficiently around the pillars without interruptions.

scholarly journals Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review (Preprint)

2019 ◽  
Author(s):  
Leila Ismail ◽  
Huned Materwala ◽  
Achim P Karduck ◽  
Abdu Adem
Keyword(s):  
Big Data ◽  
Data Management ◽  
Real Time ◽  
Management System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems

BACKGROUND Over the last century, disruptive incidents in the fields of clinical and biomedical research have yielded a tremendous change in health data management systems. This is due to a number of breakthroughs in the medical field and the need for big data analytics and the Internet of Things (IoT) to be incorporated in a real-time smart health information management system. In addition, the requirements of patient care have evolved over time, allowing for more accurate prognoses and diagnoses. In this paper, we discuss the temporal evolution of health data management systems and capture the requirements that led to the development of a given system over a certain period of time. Consequently, we provide insights into those systems and give suggestions and research directions on how they can be improved for a better health care system. OBJECTIVE This study aimed to show that there is a need for a secure and efficient health data management system that will allow physicians and patients to update decentralized medical records and to analyze the medical data for supporting more precise diagnoses, prognoses, and public insights. Limitations of existing health data management systems were analyzed. METHODS To study the evolution and requirements of health data management systems over the years, a search was conducted to obtain research articles and information on medical lawsuits, health regulations, and acts. These materials were obtained from the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, Elsevier, MEDLINE, PubMed, Scopus, and Web of Science databases. RESULTS Health data management systems have undergone a disruptive transformation over the years from paper to computer, web, cloud, IoT, big data analytics, and finally to blockchain. The requirements of a health data management system revealed from the evolving definitions of medical records and their management are (1) medical record data, (2) real-time data access, (3) patient participation, (4) data sharing, (5) data security, (6) patient identity privacy, and (7) public insights. This paper reviewed health data management systems based on these 7 requirements across studies conducted over the years. To our knowledge, this is the first analysis of the temporal evolution of health data management systems giving insights into the system requirements for better health care. CONCLUSIONS There is a need for a comprehensive real-time health data management system that allows physicians, patients, and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis or diagnosis of the diseases and the prediction of diseases. The prediction results will help in the development of an effective prevention plan.

Design and Realization of a Distributed Big Data Management System

2014 ◽  
Vol 1030-1032 ◽  
pp. 1900-1904 ◽  
Author(s):  
Hai Yan Chen
Keyword(s):  
Big Data ◽  
Data Management ◽  
Real Time ◽  
Management System ◽  
Distributed Database ◽  
Data Management System ◽  
Processing Capacity ◽  
Relation Database ◽  
Mass Data ◽  
Permanent Storage

Big Data provides a possibility of handling mass data, which acts as a subversive technique. By the way, traditional relation database is no more effective of mass data that causes distributed database NoSQL to appear and evolve. In this article, we will design and realize a new distributed big data management system (DBDMS), which is based on Hadoop and NoSQL techniques, and it provides big data real-time collection, search and permanent storage. Proved by some experiment, DBDMS can enhance the processing capacity of mass data, very suitable for mass log backup and retrieval, mass network packet grab and analyze, and etc. other applied areas.

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