XML message filtering systems are used for sifting through real-time messages to support business data mining and reporting. An XML message filtering system needs to (a) process registered filter predicates on multiple distributed real-time streams and (b) match and validate the filter results with local data to identify the relevant data that can be used for higher-level processing. Although efficient real-time filtering schemes exists, the
phase of the operation where filter results have to be matched against local data to select those matches that are relevant to the particular task remains to be expensive as it requires expensive join operations. In this paper, we present an efficient middleware (
) for filtering and matching XML messages against locally available data. The proposed operator relies on a novel cluster-domain matching scheme to reduce the cost of the process. We analytically study the cost of the proposed middleware and experimentally show that it adaptively reduces the number of local data accesses and provides large savings in matching time with respect to cluster-unaware matching.