Noise Removal in Distributed Time Series Database Using Predominant Pattern Distribution Model
B. Sujatha · IOSR Journal of Engineering · 2013
A time series database is a collection of well-defined data sets obtained through repeated measurements of time.The data to be examined are regularly noisy and diverse periodicity types.The existing suffix tree based periodic pattern mining algorithm can detect symbol, sequence and segment periodicity in time series data with noise filters for diverse noise kinds.But the running time desired to identify the patterns without redundancy is high.To overcome this issue, in this paper, predominant pattern distribution model is introduced with which redundant and unwanted noisy patterns are identified and discarded from it.Predominant patterns are extracted with automatic or user defined threshold of pattern of interest, generated from the dynamic online time series data.Performance of proposed framework is measured and evaluated in terms of periodic pattern mining accuracy, noise distribution rate, and predominant pattern occurrence.