Automating the design of graphical presentations of relational information
ACM Transactions on Graphics (TOG)
Task-analytic approach to the automated design of graphic presentations
ACM Transactions on Graphics (TOG)
Interactive graphic design using automatic presentation knowledge
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Beautiful Evidence
The Cognitive Style of PowerPoint: Pitching Out Corrupts Within, Second Edition
The Cognitive Style of PowerPoint: Pitching Out Corrupts Within, Second Edition
ManyEyes: a Site for Visualization at Internet Scale
IEEE Transactions on Visualization and Computer Graphics
TIMELINES: Tag clouds and the case for vernacular visualization
interactions - Changing energy use through design
Case Provenance: The Value of Remembering Case Sources
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Provenance, Trust, and Sharing in Peer-to-Peer Case-Based Web Search
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Behavior-driven visualization recommendation
Proceedings of the 14th international conference on Intelligent user interfaces
Automated generation of graphic sketches by example
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Increasingly, in our everyday lives, we rely on our ability to access and understand complex information. Just as the search engine played a key role in helping people access relevant information, there is evidence that the next generation of information tools will provide users with a greater ability to analyse and make sense of large amounts of raw data. Visualization technologies are set to play an important role in this regard. However, the current generation of visualization tools are simply too complex for the typical user. In this paper we describe a novel application of case-based reasoning techniques to help users visualize complex datasets. We exploit an online visualization service, ManyEyes, and explore how case-based representation of datasets including simple features such as size and content types can produce recommendations to assist novice users in the selection of appropriate visualization types.