Paper Info
Title
Visual exploration of stream pattern changes using a data-driven framework
Abstract
When using visualization techniques to explore data streams, an important task is to convey pattern changes. Challenges include: (1) Most data analysis tasks require users to observe the pattern change over a long time range; (2) The change rate of patterns is not a constant, and most users are normally more interested in bigger changes than smaller ones. Although distorting the time axis as proposed in the literature can partially solve this problem, most of these are driven by the user. This is however not applicable to streaming data exploration tasks that normally require near real-time responsiveness. In this paper, we propose a data-driven framework to merge and thus condense time windows having small or no changes. Only significant changes are shown to users. Juxtaposed views are discussed for conveying data pattern changes. Our experiments show that our merge algorithm preserves more change information than uniform sampling. We also conducted a user study to confirm that our proposed techniques can help users find pattern changes more quickly than via a non-distorted time axis.
Year
DOI
Venue
2010
10.1007/978-3-642-17274-8_51
ISVC
Keywords
Field
DocType
non-distorted time axis,data-driven framework,conveying data pattern change,data analysis task,pattern change,bigger change,data exploration task,stream pattern change,change information,long time range,condense time windows,data stream,visual exploration,near real time,data analysis
Merge algorithm,Data mining,Data stream mining,Data-driven,Data patterns,Data stream,Computer science,Real-time computing,Sampling (statistics),Merge (version control),Creative visualization
Conference
Volume
ISSN
ISBN
6454
0302-9743
3-642-17273-3
Citations 
PageRank 
References 
5
0.44
9
Authors
3
Name
Order
Citations
PageRank
Zaixian Xie1444.05
Matthew O. Ward21757189.48
Elke A. Rundensteiner34076700.65