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.
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Acknowledgments
We deeply appreciate the help of our colleagues and friends who reviewed the paper. A special mention to Uma Gopalakrishnan who provided important suggestions for this work. We are grateful to the Chancellor of Amrita University, Sri Mata Amritanandamayi Devi, for constant support and encouragement in conducting research that has direct societal impact.
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Pathinarupothi, R.K., Alangot, B., Ramesh, M.V., Achuthan, K., Rangan, P.V. (2016). H-Plane: Intelligent Data Management for Mobile Healthcare Applications. In: Younas, M., Awan, I., Kryvinska, N., Strauss, C., Thanh, D. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2016. Lecture Notes in Computer Science(), vol 9847. Springer, Cham. https://doi.org/10.1007/978-3-319-44215-0_23
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DOI: https://doi.org/10.1007/978-3-319-44215-0_23
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