Multi-filters collaboration for querying XML data streams

Current methods for querying XML data streams are mostly based on events filtering techniques. It is unavoidable for the query systems to adopt an event filter to process the incoming XML tags. It is well known that during the filtering, some data items have to be buffered before the filter can make the proper decision for adopting strategies to deal with them. Furthermore, for a single filter system, the buffer size often increases exponentially in the real application. Lots of work both theoretically and empirically, has been done to dissolve or depress that exponential explosion, but it still remains an active research topic. Considering the fast development of computer supported collaboration, we propose a new multi-filters collaborative querying technique to process XML data streams. We show that the multi-filters collaboration can effectively share and reduce the filtering space consumption if some XML document and query constraints can be satisfied. Deploying our multi-filters collaboration technique, the querying systems together can break the limitation of the concurrency lower bound, an important theoretic lower bound within XML data streams research field. The empirical study shown in this paper demonstrates that our multi-filters collaboration outperforms the limitation of the concurrency lower bound.