Similarity search in streaming time series with the support of Skyline index
by Duong Tuan Anh; Tran Thi Thanh Nga
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 9, No. 1, 2014

Abstract: The similarity search problem in streaming time series has become an interesting research topic because such data arise in so many applications of various areas. In this problem, the fact that data streams are updated continuously as new data arrive in real time is a challenge because of dimensionality reduction recalculation and index update costs. In this paper, using ideas of a delayed update policy on R*-tree proposed by Kontaki et al., we proposed an improved method in which indexable piecewise linear approximation (PLA) dimensionality reduction method with the support of Skyline index can be used to perform effectively the similarity search task in streaming time series. Experimental results show that the similarity search in streaming time series with the support of Skyline index is more efficient than the case of using R*-tree.

Online publication date: Wed, 30-Jul-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com