Parallel volume ray-casting for unstructured-grid data on distributed-memory architectures
PRS '95 Proceedings of the IEEE symposium on Parallel rendering
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Principles of distributed database systems (2nd ed.)
Principles of distributed database systems (2nd ed.)
Guest Editors' Introduction: Graphics Applications for Grid Computing
IEEE Computer Graphics and Applications
Grid-Distributed Visualizations Using Connectionless Protocols
IEEE Computer Graphics and Applications
VisTrails: visualization meets data management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
QEF - Supporting Complex Query Applications
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
MapReduce: a flexible data processing tool
Communications of the ACM - Amir Pnueli: Ahead of His Time
An adaptive distributed query processing grid service
DMG 2005 Proceedings of the First VLDB conference on Data Management in Grids
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Scientific Visualization is a computer-based field concerned with techniques that allow scientists to create graphical representations from datasets generated by computational simulations or acquisition instruments. To address the computational cost of visualization tasks, specially for large datasets, researchers have explored grid environments as a platform for their parallel evaluation. It is however not trivial to adapt each different visualization technique to run in grid environments. A desirable alternative would separate the specificities of data and process distribution in grids from visualization computation logic. In this work we claim that the QEF (query evaluation framework) leverages scientific visualization computation with the above mentioned characteristics. Visualization computation techniques are modeled as operators in an algebra and integrated with a set of control operators that manage data distribution leading to a parallel QEP (query execution plan). We show the benefits of parallelization for two of those techniques: particle tracing and volume rendering. For these techniques, our experiments demonstrate many positive aspects of the solution presented, as well as opportunities for future work.