Issues in data stream management
ACM SIGMOD Record
Connecting time-oriented data and information to a coherent interactive visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
BinX: Dynamic Exploration of Time Series Datasets Across Aggregation Levels
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Visualizing Live Text Streams Using Motion and Temporal Pooling
IEEE Computer Graphics and Applications
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Dynamic visualization of transient data streams
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Multi-resolution techniques for visual exploration of large time-series data
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Density displays for data stream monitoring
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
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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.