Name
Abstract | ||
|---|---|---|
We present an intelligent data management framework that can facilitate development of highly scalable and mobile healthcare applications for remote monitoring of patients. This is achieved through the use of a global log data abstraction that leverages the storage and processing capabilities of the edge devices and the cloud in a seamless manner. In existing log based storage systems, data is read as fixed size chunks from the cloud to enhance performance. However, in healthcare applications, where the data access pattern of the end users differ widely, this approach leads to unnecessary storage and cost overheads. To overcome these, we propose dynamic log chunking. The experimental results, comparing existing fixed chunking against the H-Plane model, show 13%-19% savings in network bandwidth as well as cost while fetching the data from the cloud. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-44215-0_23 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Cloud computing,Healthcare IoT framework,Log storage system | Mobile computing,Data mining,Computer science,Bandwidth (signal processing),Edge device,Chunking (psychology),Data management,Data access,Cloud computing,Scalability,Distributed computing | Conference |
Volume | ISSN | Citations |
9847 | 0302-9743 | 1 |
PageRank | References | Authors |
0.40 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rahul Krishnan Pathinarupothi | 1 | 10 | 5.55 |
| Bithin Alangot | 2 | 3 | 2.52 |
| Ramesh Maneesha | 3 | 63 | 22.44 |
| Krishnashree Achuthan | 4 | 68 | 24.70 |
| P. Venkat Rangan | 5 | 1057 | 235.37 |