DataMeadow: a visual canvas for analysis of large-scale multivariate data
Information Visualization - Special issue on visual analytics science and technology
Design considerations for collaborative visual analytics
Information Visualization - Special issue on visual analytics science and technology
Creation and Collaboration: Engaging New Audiences for Information Visualization
Information Visualization
Visual Verification of Hypotheses
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
An Algorithm for Treating Uncertainties in the Visualization of Pipeline Sensors' Datasets
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Visual data mining and discovery in multivariate data using monotone n-D structure
KONT'07/KPP'07 Proceedings of the First international conference on Knowledge processing and data analysis
Event-based concepts for user-driven visualization
Information Visualization
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Visualization systems traditionally focus on graphical representation of information. They tend not to provide integrated analytical services that could aid users in tackling complex knowledge discovery tasks. Users' exploration in such environments is usually impeded due to several problems: 1) valuable information is hard to discover when too much data is visualized on the screen; 2) Users have to manage and organize their discoveries off line, because no systematic discovery management mechanism exists; 3) their discoveries based on visual exploration alone may lack accuracy; 4) and they have no convenient access to the important knowledge learned by other users. To tackle these problems, it has been recognized that analytical tools must be introduced into visualization systems. In this paper, we present a novel analysis-guided exploration system, called the Nugget Management System (NMS). It leverages the collaborative effort of human comprehensibility and machine computations to facilitate users' visual exploration processes. Specifically, NMS first extracts the valuable information (nuggets) hidden in datasets based on the interests of users. Given that similar nuggets may be re-discovered by different users, NMS consolidates the nugget candidate set by clustering based on their semantic similarity. To solve the problem of inaccurate discoveries, localized data mining techniques are applied to refine the nuggets to best represent the captured patterns in datasets. Lastly, the resulting well-organized nugget pool is used to guide users' exploration. To evaluate the effectiveness of NMS, we integrated NMS into XmdvTool, a freeware multivariate visualization system.