Optimizing differential XML processing by leveraging schema and statistics

Abstract

XML fills a critical role in many software infrastructures such as SOA (Service-Oriented Architecture), Web Services, and Grid Computing. In this paper, we propose a high performance XML parser used as a fundamental component to increase the viability of such infrastructures even for mission-critical business applications. We previously proposed an XML parser based on the notion of differential processing under the hypothesis that XML documents are similar to each other, and in this paper we enhance this approach to achieve higher performance by leveraging static information as well as dynamic information. XML schema languages can represent the static information that is used for optimizing the inside state transitions. Meanwhile, statistics for a set of instance documents are used as dynamic information. These two approaches can be used in complementary ways. Our experimental results show that each of the proposed optimization techniques is effective and the combination of multiple optimizations is especially effective, resulting in a 73.2% performance improvement compared to our earlier work. © 2006 Springer-Verlag.