Dynamic queries for information exploration: an implementation and evaluation
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic information visualization
ACM SIGMOD Record
An interactive visual query environment for exploring data
Proceedings of the 10th annual ACM symposium on User interface software and technology
Quintary trees: a file structure for multidimensional datbase sytems
ACM Transactions on Database Systems (TODS)
LATIN '98 Proceedings of the Third Latin American Symposium on Theoretical Informatics
Query Previews in Networked Information Systems
ADL '96 Proceedings of the 3rd International Forum on Research and Technology Advances in Digital Libraries
Design and Evaluation of Incremental Data Structures and Algorithms for Dynamic Query Interfaces
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
Toward an information visualization workspace: combining multiple means of expression
Human-Computer Interaction
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
Scalable Visual Hierarchy Exploration
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
An interactive visualization method of numerical data based on natural language requirements
International Journal of Human-Computer Studies - Special issue on HCI research in Japan
Scented Widgets: Improving Navigation Cues with Embedded Visualizations
IEEE Transactions on Visualization and Computer Graphics
Empirical comparison of dynamic query sliders and brushing histograms
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Understanding interactive legends: a comparative evaluation with standard widgets
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
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A new method is presented to get insight into univariate time series data. The problem addressed here is how to identify patterns and trends on multiple time scales (days, weeks, seasons) simultaneously. The solution presented is to cluster similar daily ...