A visual analytics system for financial time-series data
Proceedings of the 3rd International Symposium on Visual Information Communication
LifeFlow: visualizing an overview of event sequences
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Visual signatures for financial time series
Proceedings of the 2011 Visual Information Communication - International Symposium
Querying event sequences by exact match or similarity search: Design and empirical evaluation
Interacting with Computers
Visual comparison for information visualization
Information Visualization - Special issue on State of the Field and New Research Directions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Storygraph: extracting patterns from spatio-temporal data
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
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The identification of significant sequences in large and complex event-based temporal data is a challenging problem with applications in many areas of today's information intensive society. Pure visual representations can be used for the analysis, but are constrained to small data sets. Algorithmic search mechanisms used for larger data sets become expensive as the data size increases and typically focus on frequency of occurrence to reduce the computational complexity, often overlooking important infrequent sequences and outliers. In this paper we introduce an interactive visual data mining approach based on an adaptation of techniques developed for web searching, combined with an intuitive visual interface, to facilitate user-centred exploration of the data and identification of sequences significant to that user. The search algorithm used in the exploration executes in negligible time, even for large data, and so no pre-processing of the selected data is required, making this a completely interactive experience for the user. Our particular application area is social science diary data but the technique is applicable across many other disciplines.