Cloud-based Dashboard for Medical Data Center
Manishankar S1, B Unnikrishnan2, Sreenadh M3
1Manishankar S, Department of Computer Science, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham Mysuru, (Karnataka), India.
2B Unnikrishnan, Department of Computer Science, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham Mysuru, (Karnataka), India.
3Sreenadh M, Department of Computer Science, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham Mysuru, (Karnataka), India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 1432-1437 | Volume-8 Issue-8, June 2019 | Retrieval Number: H7511068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Introduction of technology to aid medical field is one of the revolutionary changes in modern information era. Digitizing or storing medical data in to centralized storage has helped greatly in maintaining medical records and improving the hospital treatment facilities. The data may be collected as raw textual records, spread sheets, images and, videos, also the data is many a time real time data that is retrieved from sensors or IoT devices in case of Telemedicine data center. The research carried out proposes an efficient storage approach for medical data centers especially with respect to Telemedicine Scenario with help a Cloud based Dashboard or API. The proposed data categorization algorithm clusters the data in to varies categories and store them in to different partitions in Cloud data center. Partitioning of Cloud data center helps in retrieving the data as well as processing or analyzing the data in a later stage. Implementation is carried out with the help of Fire base cloud storage and KNN classifier for categorizing the data and partitions are created with the help of ingestion time and data category identified by the proposed categorization algorithm. The results obtained by testing with various data shows that the proposed Cloud storage with the dashboard is efficient in terms of retrieval time and memory utilization than many other public cloud platforms.
Keyword: medical; cloud; categorization; KNN; dashboard.
Scope of the Article: Cloud Computing.