Abstract
XSLT is an increasingly popular language for processing XML data. It is widely supported by application platform software. However, little optimization effort has been made inside the current XSLT processing engines. Evaluating a very simple XSLT program on a large XML document with a simple schema may result in extensive usage of memory. In this paper, we present a novel notion of Streaming Processing Model (SPM) to evaluate a subset of XSLT programs on XML documents, especially large ones. With SPM, an XSLT processor can transform an XML source document to other formats without extra memory buffers required. Therefore, our approach can not only tackle large source documents, but also produce large results. We demonstrate with a performance study the advantages of the SPM approach. Experimental results clearly confirm that SPM improves XSLT evaluation typically 2 to 10 times better than the existing approaches. Moreover, the SPM approach also features high scalability.
This work is supported in part by the National Hi-Tech Research and Development Program of China under Grant No. 2002AA116020 and by the National Natural Science Foundation of China under Grant No. 60228006.
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Guo, Z., Li, M., Wang, X., Zhou, A. (2004). Scalable XSLT Evaluation. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds) Advanced Web Technologies and Applications. APWeb 2004. Lecture Notes in Computer Science, vol 3007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24655-8_20
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DOI: https://doi.org/10.1007/978-3-540-24655-8_20
Publisher Name: Springer, Berlin, Heidelberg
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