Optimal File-Bundle Caching Algorithms for Data-Grids
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
Variable Interactions in Query-Driven Visualization
IEEE Transactions on Visualization and Computer Graphics
On the performance of bitmap indices for high cardinality attributes
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
High performance multivariate visual data exploration for extremely large data
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Analysis of Basic Data Reordering Techniques
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Dynamic data organization for bitmap indices
Proceedings of the 3rd international conference on Scalable information systems
Finding Regions of Interest in Large Scientific Datasets
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
File caching in data intensive scientific applications on data-grids
DMG 2005 Proceedings of the First VLDB conference on Data Management in Grids
Taming massive distributed datasets: data sampling using bitmap indices
Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
SDQuery DSI: integrating data management support with a wide area data transfer protocol
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Many scientific applications generate large spatio-temporal datasets. A common way of exploring these datasets is to identify and track regions of interest. Usually these regions are defined as contiguous sets of points whose attributes satisfy some user defined conditions, e.g. high temperature regions in a combustion simulation. At each time step, the regions of interest may be identified by first searching for all points that satisfy the conditions and then grouping the points into connected regions. To speed up this process, the searching step may use a tree-based indexing scheme, such as a KD-tree or an Octree. However, these indices are efficient only if the searches are limited to one or a small number of selected attributes. Scientific datasets often contain hundreds of attributes and scientists frequently study these attributes in complex combinations, e.g. finding regions of high temperature and low pressure. Bitmap indexing is an efficient method for searching on multiple criteria simultaneously. We apply a bitmap compression scheme to reduce the size of the indices. In addition, we showthat the compressed bitmaps can be used efficiently to perform the region growing and the region tracking operations. Analyses show that our approach scales well and our tests on two datasets from simulation of the autoignition process show impressive performance.