Relaxed selection techniques for querying time-series graphs

Time-series graphs are often used to visualize phenomena that change over time. Common tasks include comparing values at different points in time and searching for specified patterns, either exact or approximate. However, tools that support time-series graphs typically separate query specification from the actual search process, allowing users to adapt the level of similarity only after specifying the pattern. We introduce relaxed selection techniques, in which users implicitly define a level of similarity that can vary across the search pattern, while creating a search query with a single-gesture interaction. Users sketch over part of the graph, establishing the level of similarity through either spatial deviations from the graph, or the speed at which they sketch (temporal deviations). In a user study, participants were significantly faster when using our temporally relaxed selection technique than when using traditional techniques. In addition, they achieved significantly higher precision and recall with our spatially relaxed selection technique compared to traditional techniques.

[1]  Ben Shneiderman,et al.  Interactive Exploration of Time Series Data , 2003 .

[2]  Paolo Buono,et al.  Interactive shape specification for pattern search in time series , 2008, AVI '08.

[3]  Shigeru Makino,et al.  QueryLines: approximate query for visual browsing , 2005, CHI 2005.

[4]  Eamonn J. Keogh,et al.  VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases , 2004, VLDB.

[5]  Wei Pan,et al.  A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments , 2002, Bioinform..

[6]  Jessica Lin,et al.  Visually mining and monitoring massive time series , 2004, KDD.

[7]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[8]  Eamonn J. Keogh,et al.  Relevance feedback retrieval of time series data , 1999, SIGIR '99.

[9]  Alexandros Chortaras.  Efficient Storage, Retrieval and Indexing of Time Series Data , 2002 .

[10]  Robert Kincaid,et al.  Line graph explorer: scalable display of line graphs using Focus+Context , 2006, AVI '06.

[11]  Eamonn J. Keogh,et al.  Locally adaptive dimensionality reduction for indexing large time series databases , 2001, SIGMOD '01.

[12]  Tiziana Catarci,et al.  Visualization of linear time-oriented data: a survey , 2000, Proceedings of the First International Conference on Web Information Systems Engineering.

[13]  Hagit Shatkay,et al.  Approximate queries and representations for large data sequences , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[14]  Jessica Lin,et al.  Finding Motifs in Time Series , 2002, KDD 2002.

[15]  Ben Shneiderman,et al.  Interactive pattern search in time series , 2005, IS&T/SPIE Electronic Imaging.

[16]  Ben Shneiderman,et al.  Dynamic Query Tools for Time Series Data Sets: Timebox Widgets for Interactive Exploration , 2004, Inf. Vis..

[17]  Jeffrey P. Morrill.  Distributed recognition of patterns in time series data , 1998, CACM.

[18]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[19]  Haixun Wang,et al.  Landmarks: a new model for similarity-based pattern querying in time series databases , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[20]  Ben Shneiderman,et al.  Dynamic querying for pattern identification in microarray and genomic data , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[21]  Ben Shneiderman,et al.  An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data , 2002, FQAS.

[22]  Martin Wattenberg,et al.  Sketching a graph to query a time-series database , 2001, CHI Extended Abstracts.