The past decade has seen a lot of research on various time series representations. Various researches have been carried out that focused on representations that are processed in batch mode and visualize each value with almost equal dependability. As the tremendous usage of mobile devices and real time sensors has released the necessity and importance for representations that can simultaneously be updated, and can estimate the time oriented data with reliability and proportional to its time period for extended analysis. The approximation property of time series data allows us to answer queries more effectively about the recent data with higher precision, since in many domains recent information is more useful than older information. We call such incoming data as amnesic.
However we have to fetch the required information from amnesic data as it consists of greater value for data analysis. In this paper, we introduce a novel approach of time series analysis that can summarize the incoming streaming data and represent the processed streams as user-specified amnesic functions. We propose algorithms for monitoring and handling streaming time series data and summarizing them for performing user driven analysis. As our focus is on handling streaming data and summarizing the streams, we suggest that processed streams to be forwarded to appropriate visualization and plot them in streaming visualization.I. INTRODUCTION Recent advances in both hardware and software have allowed huge rise in streaming data processing.
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