Optimizing information agents by selectively materializing data

We present an approach for optimizing the performance of information agents by materializing useful informa-tion. A critical problem with information agents, par-ticularly those gathering and integrating information from Web sources is a high query response time. This is because the data needed to answer user queries is present across several different Web sources (and in several pages within a source) and retrieving,extracting and integrating the data is time consuming. We ad-dress this problem by materializing useful classes of in-formation and defining them as auxiliary data sources for the information agent. The key challenge here is to identify the content and schema of the classes of infor-mation that would be useful to materialize. We present an algorithm that identifies such classes by analyzing patterns in user queries. We describe an implementa-tion of our approach and experiments in progress. We also discuss other important problems that we will ad-dress in optimizing information agents