Parallel Data Systems with Self-Generating Analytics : Demo of self-generating analytics on the hospital supply chain ecosystem

dc.contributor.authorBaker, F.
dc.contributor.authorChandrasekaran, K.
dc.date.accessioned2026-02-06T06:35:55Z
dc.date.issued2021
dc.description.abstractWe 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.citationTENSYMP 2021 - 2021 IEEE Region 10 Symposium, 2021, Vol., , p. -
dc.identifier.urihttps://doi.org/10.1109/TENSYMP52854.2021.9550910
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/30113
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectDDM
dc.subjectdistributed data
dc.subjecthospital management
dc.subjectselfgenerating analytics
dc.titleParallel Data Systems with Self-Generating Analytics : Demo of self-generating analytics on the hospital supply chain ecosystem

Files