Visualization And Analysis Of Compression And Amplification Of The Time-varying Data

With the development of intelligent equipment,large number of time-varying data is accumulated in medical treatment,education,public services and other social field.Due to the large amount of the data,we need to compress and then store it.Meanwhile,on account of the time-varying data changes a lot,finding and visualizing features in it is a research hotspot in big data processing and visualization analysis.Taking the illustration of time-varying data as an entry point,we develop a visualization system to amplify the small patterns in time-varying data.At the same time,we propose a system based on diffusion curves to compress the vector field data.The specific work is as follows:Firstly,we put forward a method which can amplify and visualize small signals in time-varying data.By selecting the amplified region and amplification frequency automatically,the system can illustrate the micro characteristics hidden in other signals.We design a visualization interface to analyze and explore the data expediently.The system can discover the desired features with a time varying filter,and the irrelevant features can be concealed similarly.Also,the visualization system enables us to interact with each other while analyzing the features.Secondly,we present a visualization technique for representing the whole flow field data with little feature curves based on diffusion curves and analysis.The system can extract the critical points and contour lines in the vector field data,and then solve the Poisson equation with less boundary conditions,finally it can achieve the initial compression of the field data.Based on the above methods,we develop a visualization system.We input the stream data interactively,then the system can optimize the effect of the reduction curve step by step with our help.It shows optimal reconstruction performance after manually selecting seed points and several iterations.