HiSbase: histogram-based P2P main memory data management
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Challenges in dependable internet-scale stream processing
Proceedings of the 2nd workshop on Dependable distributed data management
Proceedings of the VLDB Endowment
Generalizing the data management of three community grids
Future Generation Computer Systems
Scalable community-driven data sharing in e-science grids
Future Generation Computer Systems
Workload-aware data partitioning in community-driven data grids
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Flexible and scalable storage management for data-intensive stream processing
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Collaborative query coordination in community-driven data grids
Proceedings of the 18th ACM international symposium on High performance distributed computing
Business-driven short-term management of a hybrid IT infrastructure
Journal of Parallel and Distributed Computing
Proceedings of the 16th International ACM Sigsoft symposium on Component-based software engineering
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The field of e-science currently faces many challenges. Among the most important ones are the analysis of huge volumes of scientific data and the connection of various sciences and communities, thus enabling scientists to share scientific interests, data, and research results. These issues can be addressed by processing large data volumes on-thefly in the form of data streams and by combining multiple data sources and making the results available in a network. In this paper, we demonstrate how e-science can benefit from research in computer science in the field of data stream management. In particular, we are concerned with processing multiple data streams in grid-based peer-to-peer (P2P) networks. We introduce spatial matching, which is a current issue in astrophysics, as a real-life e-science scenario to show how a data stream management system (DSMS) can help in efficiently performing associated tasks. We describe our new way of solving the spatial matching problem and present some evaluation results. In the course of the evaluation, our DSMS StarGlobe proves to be a valuable computing platform for astrophysical applications.