SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
MOCHA: a self-extensible database middleware system for distributed data sources
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
R-tree-based data migration and self-tuning strategies in shared-nothing spatial databases
Proceedings of the 9th ACM international symposium on Advances in geographic information systems
Distributed processing of very large datasets with DataCutter
Parallel Computing - Clusters and computational grids for scientific computing
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Processing large-scale multi-dimensional data in parallel and distributed environments
Parallel Computing - Parallel data-intensive algorithms and applications
On the Multiple-Query Optimization Problem
IEEE Transactions on Knowledge and Data Engineering
Declustering Spatial Databases on a Multi-Computer Architecture
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Semantic Data Caching and Replacement
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Active Proxy-G: optimizing the query execution process in the grid
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
A high performance multi-perspective vision studio
ICS '03 Proceedings of the 17th annual international conference on Supercomputing
Master-Client R-Trees: A New Parallel R-Tree Architecture
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Optimizing the Execution of Multiple Data Analysis Queries on Parallel and Distributed Environments
IEEE Transactions on Parallel and Distributed Systems
A Comparative Study of Spatial Indexing Techniques for Multidimensional Scientific Datasets
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Spatial indexing of distributed multidimensional datasets
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
Query planning for the grid: adapting to dynamic resource availability
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
DiST: fully decentralized indexing for querying distributed multidimensional datasets
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
IEEE Journal on Selected Areas in Communications
Journal of Parallel and Distributed Computing
Multiple query scheduling for distributed semantic caches
Journal of Parallel and Distributed Computing
An efficient multi-tier tablet server storage architecture
Proceedings of the 2nd ACM Symposium on Cloud Computing
A time cost optimization for similar scenarios mobile GIS queries
Journal of Visual Languages and Computing
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MQO is a distributed multiple query processing middleware that can use resources available on the Grid to optimize query processing for data analysis and visualization applications. It does so by introducing one or more proxies that act as front-ends to a collection of backend servers. The basic idea behind this architecture is active semantic caching, whereby queries can leverage available cached results in the proxy either directly or through transformations. While this approach has been shown to speed up query evaluation under multi-client workloads, the caching infrastructure in the backend servers is not used well for query processing. Because this collective caching infrastructure scales with the number of servers, it is an important asset. In this paper, we describe a distributed multidimensional indexing scheme that enables the proxy to directly consider the cache contents available at the backend servers for query planning and scheduling. This approach is shown to produce better query plans and faster query response times as we experimentally demonstrate.