Representing Multidimensional Cancer Registry Data
Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies
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Christian Chabot addresses the common misperceptions and stereotypes about visual analytics as the practice begins moving into widespread use. People typically associate visual analytics with four principles, all of which are false: the practice requires massive data, it's used to understand only complex problems, it requires the invention of new visual paradigms, and it focuses on finding hidden insights. Chabot explains why these principles are incorrect and why the visual analytics community would help the world at large by espousing its virtues.