C. S. Sindhu
ABSTRACT
We are inundated in a flood of data today. Data is being collected at a rapid scale from variety of sources like healthcare, e-commerce, social networking and so on. Decisions which were earlier made on assumptions can now be made on the data itself. It's a well known fact that volume, variety, velocity and veracity are the challenges associated in handling Big Data. The dynamic nature of the Internet and the velocity factor pose humongous challenges in retrieving patterns from the data. Coping up with noisy data which occurs at a rapid rate is still an open challenge. We have handled the issues associated with variety and veracity. After reviewing the existing system, it was found that there is no significant research model towards addressing data velocity problem exclusively taking case study of healthcare analytics.
Hence, this paper presents a novel framework TAF or Temporal Analysis Framework that mainly targets at handling the incoming speed of data and redundancies in Healthcare Analytics. The proposed system uses real-time data analysis that significantly handles the data velocity along with retention of minimal error. The study outcome was assessed to find minimal algorithm complexities compared to any system that doesn't use this approach of self-adaptable real-time data analysis.
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