Representing unevenly-spaced time series data for visualization and interactive exploration

Authors:
Aleks Aris;Ben Shneiderman;Catherine Plaisant;Galit Shmueli;Wolfgang Jank
Affiliations:
Human-Computer Interaction Laboratory, University of Maryland Institute for Advanced Computer Studies;Human-Computer Interaction Laboratory, University of Maryland Institute for Advanced Computer Studies;Human-Computer Interaction Laboratory, University of Maryland Institute for Advanced Computer Studies;Department of Decision and Information Technologies, Robert H. Smith School of Business, University of Maryland, College Park, MD;Department of Decision and Information Technologies, Robert H. Smith School of Business, University of Maryland, College Park, MD
Venue:
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
Year:
2005

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Abstract

Visualizing time series is useful to support discovery of relations and patterns in financial, genomic, medical and other applications. Often, measurements are equally spaced over time. We discuss the challenges of unevenly-spaced time series and present fourrepresentationmethods: sampled events, aggregated sampled events, event index and interleaved event index. We developed these methods while studying eBay auction data with TimeSearcher. We describe the advantages, disadvantages, choices for algorithms and parameters, and compare the different methods for different tasks. Interaction issues such as screen resolution, response time for dynamic queries, and learnability are governed by these decisions.