We have architected and evaluated a new kind of data resource, one that is composed of a logical collection of ephemeral data streams that could be viewed as a collection of publish-subscribe "channels" over which rich data-access and semantic operations can be performed. This paper contributes new insight to stream processing under the highly asynchronous stream workloads often found in data-driven scientific applications, and presents insights gained through porting a distributed stream processing system to a grid services framework. Experimental results reveal limits on stream processing rates that are directly tied to differences in stream rates.

The contribution of this work has two folds. First, we extend the current query planners' cost metric space by introducing network bandwidth cost, query deployment cost and query re-using cost; second, we develop a suite of algorithms for re-using existing query fragments under different scenarios. One of the most important reusable queries is called structure-sharable query.

This paper presents an experimental evaluation of a grid-based continuous query solution to access data streams. The results presented in this study are mixed, however. We are migrating to a netCDF-based streaming model to measure system behavior in a setting that more accurately reflects a real use scenario. We are also working on enabling approximate query processing support to Calder to deal with sudden drop offs and changes in stream rates.

Management of Grid resource information is a challenging, and important area considering the potential size of the Grid and wide range of resources that should be represented. Though example Grid Information Servers exist, behavior of these servers across different platforms is less well understood. This paper describes a study we undertook to compare the access language and platform capabilities for three different database platforms, relational, native XML, and LDAP, serving as a Grid information server. Our study measures query response times for a range of queries and highlights sensitivities exhibited by the different platforms to variables such as result set size and collection size.