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. © 2010 Springer-Verlag.