Using aggregation and dynamic queries for exploring large data sets
CHI '94 Conference Companion on Human Factors in Computing Systems
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
Journal of the American Society for Information Science and Technology
Dynamic Queries for Visual Information Seeking
IEEE Software
Dynamic Aggregation with Circular Visual Designs
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Aggregate Towers: Scale Sensitive Visualization and Decluttering of Geospatial Data
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Dynamic query visualisations on World Wide Web clients: a DHTML solution for maps and scattergrams
International Journal of Web Engineering and Technology
Extreme visualization: squeezing a billion records into a million pixels
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Hi-index | 0.00 |
Rapid growth of digital data collections is overwhelming the capabilities of humans to comprehend them without aid. The extraction of useful data from large raw data sets is something that humans do poorly. Aggregation is a technique that extracts important aspect from groups of data thus reducing the amount that the user has to deal with at one time, thereby enabling them to discover patterns, outliers, gaps, and clusters. Previous mechanisms for interactive exploration with aggregated data were either too complex to use or too limited in scope. This paper proposes a new technique for dynamic aggregation that can combine with dynamic queries to support most of the tasks involved in data manipulation.