Grid-Based Data Stream Processing in e-Science

Abstract

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.