TDRM: tensor-based data representation and mining for healthcare data in cloud computing environments

2017 ◽  
Vol 74 (2) ◽  
pp. 592-614 ◽  
Author(s):  
Rajinder Sandhu ◽  
Navroop Kaur ◽  
Sandeep K. Sood ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Data Representation ◽  
Healthcare Data ◽  
Computing Environments

scholarly journals MiCADO-Edge: Towards an Application-level Orchestrator for the Cloud-to-Edge Computing Continuum

2021 ◽  
Vol 19 (4) ◽  
Author(s):  
Amjad Ullah ◽  
Huseyin Dagdeviren ◽  
Resmi C. Ariyattu ◽  
James DesLauriers ◽  
Tamas Kiss ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Analysis ◽  
Video Processing ◽  
Time Management ◽  
Realistic Case ◽  
Healthcare Data ◽  
Computing Environments ◽  
Management Policies ◽  
Run Time ◽  
Monitoring Information

AbstractAutomated deployment and run-time management of microservices-based applications in cloud computing environments is relatively well studied with several mature solutions. However, managing such applications and tasks in the cloud-to-edge continuum is far from trivial, with no robust, production-level solutions currently available. This paper presents our first attempt to extend an application-level cloud orchestration framework called MiCADO to utilise edge and fog worker nodes. The paper illustrates how MiCADO-Edge can automatically deploy complex sets of interconnected microservices in such multi-layered cloud-to-edge environments. Additionally, it shows how monitoring information can be collected from such services and how complex, user- defined run-time management policies can be enforced on application components running at any layer of the architecture. The implemented solution is demonstrated and evaluated using two realistic case studies from the areas of video processing and secure healthcare data analysis.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Survey of Cloud Computing in Healthcare Sector

2017 ◽  
Vol 7 (7) ◽  
pp. 337
Author(s):  
S. Karthiga Devi ◽  
B. Arputhamary
Keyword(s):  
Cloud Computing ◽  
Healthcare Providers ◽  
High Volume ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Cloud Infrastructure ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Number Of Patients

Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

Sign in / Sign up

Export Citation Format

Share Document