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    Essay on The Development of Algorithm for Data Stream Essay

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    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.

    However, handling massive amounts of data and arriving in continuous streams poses a challenge for researchers and practitioners, due to the physical limits of the various handy and computational resources. We have seen a gro. .n, Kaushik Chakrabarti, Michael Pazzani, and Sharad Mehrotra. “Dimensionality reduction for fast similarity search in large time series databases.” Knowledge and information Systems 3, no.

    3 (2001): 263-286.28 Palpanas, Themis, Michail Vlachos, Eamonn Keogh, and Dimitrios Gunopulos. “Streaming time series summarization using user-defined amnesic functions.”Knowledge and Data Engineering, IEEE Transactions on 20, no. 7 (2008): 992-1006.29 Silva, Jonathan A.

    , Elaine R. Faria, Rodrigo C. Barros, Eduardo R. Hruschka, Andre CPLF de Carvalho, and Joao Gama. “Data stream clustering: A survey.”ACM Computing Surveys (CSUR) 46, no.

    1 (2013): 13.30 Aigner, Wolfgang, Silvia Miksch, Wolfgang Muller, Heidrun Schumann, and Christian Tominski. “Visual methods for analyzing time-oriented data.”Visualization and Computer Graphics, IEEE Transactions on 14, no. 1 (2008): 47-60.

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