文章基本信息

标题：Finding Maximal Periodic Patterns and Pruning Strategy in Spatiotemporal Databases
本地全文：下载
作者：O.Obulesu ; Dr.A.Rama Mohan Reddy ; K.Suresh 等
期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2012
卷号：2
期号：4
出版社：S.S. Mishra
摘要：In man y application s that track an d an alyz e spatiote mporal data, move ments obe y pe riodi c pattern s; the objects follow th e same routes (approxi mate ly) over regul ar ti me intervals. Pe riodic Pattern Mining or Periodicity Detection has numerous applications such as Predi ction , Forecastin g, De tection of Unu sual eve nts, etc. The periodic patte rns are de tecte d in a Time -Series database de pending on the time interval s. Existi ng approache s could n ot de tect all type s of periodicity su ch as Symbol , Sequence an d S e gmen t at a time. In this Paper, we propos e an approach to detect all types of th e periodicity in time series Databases. It al so fin ds the periodicity in the s ubsection s of the Time-S erie s very effectively. Actu ally the periodicity de tection results in the re dundant data. To remove re dundant data the re are pruning techniqu es to apply and to get the desired pattern as an ou tput. The compre hensive s tudy demonstrates the e ffectivene ss of the proposed approach . This is very time e fficient, accurate approach t han many e xisti ng approache s.
关键词：Peri odici ty Detection; S ymbol Periodicity; Sequence Periodicity; Segmen t Periodicity; Time serie s Database