AI-Based IoT Analytics on the Cloud for Diabetic Data Management System

2022 ◽  
pp. 143-161
Author(s):  
S. V. K. R. Rajeswari ◽  
Vijayakumar Ponnusamy
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Data Management ◽  
Human Body ◽  
Management System ◽  
Data Analytics ◽  
Current Data ◽  
Data Management System ◽  
Future Trends ◽  
Healthcare Application

It is very evident by looking at the current technological advancements that the interrelation and association of artificial intelligence (AI) and IoT in the Cloud have transformed the way healthcare has been working. AI and Cloud-empowered IoT boosts operational efficiency enhanced risk management. This combination creates products and services by enhancing the existing products while increasing scalability. To reduce costs, data analytics on the Cloud is much preferred in the current formation of technologies. This chapter focuses on the integration of different AI techniques in Cloud datasets for IoT data analytics. Analyzing, predicting, and making decisions by comparing the current data with historical data. The theory of AI-based IoT analytics will be much investigated with a healthcare application. Different approaches to implementing data analytics on the Cloud for a diabetic management system will be explored (human body). Finally, future trends and possible areas of research are also discussed.

Risk Management on Information Security in ABC Company

2017 ◽  
Vol 4 (1) ◽  
pp. 62-66
Author(s):  
Luyen Ha Nam
Keyword(s):  
Risk Management ◽  
Information Security ◽  
Data Management ◽  
Management System ◽  
Risk Mitigation ◽  
Data Management System ◽  
Data Centre ◽  
Long Time ◽  
Mitigation Action ◽  
Better Than

From long, long time ago until nowadays information still takes a serious position for all aspect of life, fromindividual to organization. In ABC company information is somewhat very sensitive, very important. But how wekeep our information safe, well we have many ways to do that: in hard drive, removable disc etc. with otherorganizations they even have data centre to save their information. The objective of information security is to keep information safe from unwanted access. We applied Risk Mitigation Action framework on our data management system and after several months we have a result far better than before we use it: information more secure, quickly detect incidents, improve internal and external collaboration etc.

Problem of integration of digital data analytics services: teacher's competence in work with educational data

2020 ◽  
pp. 38-49
Author(s):  
Olga Aleksandrovna Fiofanova
Keyword(s):  
Functional Analysis ◽  
Data Management ◽  
Management System ◽  
Data Analytics ◽  
Pedagogical Knowledge ◽  
Digital Data ◽  
Data Management System ◽  
National Data ◽  
Digital Resources ◽  
Big Data Technologies

In the context of the development of data-based management, the transition to decision-making models based on Big Data technologies, in the context of the development of the national data management system, the issues of educational data analytics, the problems of the fragmentation of digital data analytics services, as well as the need to develop the teacher’s competencies in working with educational data. The structural and functional analysis of digital resources of educational data with which a modern teacher should work is presented. The competencies of the teacher in the field of analysis of educational data are characterized. The concept of “Pedagogy based on data” is considered as a new area of pedagogical knowledge and a methodological basis for the development of teachers' competencies in the field of data analysis. The author analyzes the possibilities of modernization and integration of digital data analytics services in the context of the implementation of the National Data Management System in Russia.

scholarly journals THESE MODELS NEED ENTERPRISE DATA MANAGEMENT!

10.28945/3651 ◽  
2017 ◽  
Vol 6 ◽  
pp. 01
Author(s):  
Jay Hoecker ◽  
Debbie Bernal ◽  
Alex Brito ◽  
Arda Ergonen ◽  
Richard Stiftinger
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Management System ◽  
Current Data ◽  
Scientific Models ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technology Platform

The current data management systems for the life cycle of scientific models needed an upgrade. What technology platform offered the best option for an Enterprise Data Management system?

Architecture of the National Data Management System for Creating Proactive Artificial Intelligence

2019 ◽  
Vol 144 (7) ◽  
pp. 94-104
Author(s):  
Elena N. Veduta ◽  
◽  
Tatyana N. Dzhakubova ◽  
Sergey N. Evtushenko ◽  
Ekaterina S. Ryaskova ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Management ◽  
Management System ◽  
Data Management System ◽  
National Data

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.

scholarly journals Transitioning CMS to Rucio Data Managment

2020 ◽  
Vol 245 ◽  
pp. 04033
Author(s):  
Eric Vaandering
Keyword(s):  
Data Management ◽  
Management System ◽  
Current Data ◽  
Data Management System ◽  
Adoption Process ◽  
Software Project ◽  
Primary Responsibility ◽  
Data Rates ◽  
User Data ◽  
Turn Over

Following a thorough review in 2018, the CMS experiment at the CERN LHC decided to adopt Rucio as its new data management system. Rucio is emerging as a community software project and will replace an aging CMSonly system before the start-up of LHC Run 3 in 2021. Rucio was chosen after an evaluation determined that Rucio could meet the technical and scale needs of CMS. The data management system for CMS needs to manage the current data sample of approximately 200 PB of data with 1 PB of transfers per day. The data management system must also have a development path suitable for LHC Run 4 (2026) data rates, which are expected to be 50 times larger. This contribution details the ongoing CMS adoption process as we replace our legacy system with Rucio, focusing on the challenges of integrating Rucio into an established set of experimental tools and procedures. This will include the migration of metadata, the construction of interfaces to the rest of the CMS computing tools, scale tests, operations, monitoring, and the plan to gradually turn over primary responsibility for the management of data to Rucio. A description of CMS user data management with Rucio will also be provided.

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

10.2196/17508 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e17508
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.

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