Query planning with information quality bounds

Query planning for information integration using a local-as-view approach is exponential in the size of the user query. Furthermore, it may generate an exponential number of plans, many of which will produce results of very poor quality. We propose to use information quality reasoning to speed up query planning. We construct tight upper quality bounds for a branch & bound algorithm. The algorithm uses these quality scores to filter out non-promising plans early on. Experiments show that this approach dramatically improves planning time without compromising; the quality of the result.