Parallel Data Systems with Self-Generating Analytics : Demo of self-generating analytics on the hospital supply chain ecosystem
| dc.contributor.author | Baker, F. | |
| dc.contributor.author | Chandrasekaran, K. | |
| dc.date.accessioned | 2026-02-06T06:35:55Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | We attempt to produce a feasible solution for a real-world problem, using distributed data management concepts. The tool is designed to improve the quality of decision making in hospitals, suppliers, and manufacturers by presenting all information about mutual transactions in a secure and searchable manner. Since the databases of hospitals, suppliers, and manufacturers are large enough that compiling all of them would be difficult, we have applied modified aggregation and query methods for distributed data systems and produce metrics that can support growth. Such a system could help detect outbreaks of disease, produce meaningful data about drug use, and prevent wastage of medical resources. © 2021 IEEE. | |
| dc.identifier.citation | TENSYMP 2021 - 2021 IEEE Region 10 Symposium, 2021, Vol., , p. - | |
| dc.identifier.uri | https://doi.org/10.1109/TENSYMP52854.2021.9550910 | |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/30113 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject | DDM | |
| dc.subject | distributed data | |
| dc.subject | hospital management | |
| dc.subject | selfgenerating analytics | |
| dc.title | Parallel Data Systems with Self-Generating Analytics : Demo of self-generating analytics on the hospital supply chain ecosystem |
