Approximate medians and other quantiles in one pass and with limited memory
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Global static indexing for real-time exploration of very large regular grids
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Visualizing spatial distribution data sets
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
Visualizing Spatial Multivalue Data
IEEE Computer Graphics and Applications
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Approximate Clustering on Distributed Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Hadoop: The Definitive Guide
Comparative Visual Analysis of 2D Function Ensembles
Computer Graphics Forum
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The rapid and continuing increase in available high-performance computing resources has driven simulation-based science in two directions. First, the simulations themselves are growing more complex, whether in the fidelity of the models, spatiotemporal resolution or (more frequently) both. Second, multiple instances of a simulation can be run to sample the results of parameters within a given space instead of at a single point. We name the results of such a family of runs an ensemble data set. In this paper we discuss the properties of ensemble data sets, consider their implications for analysis and visualization algorithms, and present a few insights into promising avenues of investigation.