This paper describes an experimental system in which customized high performance XML parsers are prepared using parser generation and compilation techniques. Parsing is integrated with Schema-based validation and deserialization, and the resulting validating processors are shown to be as fast as or in many cases significantly faster than traditional nonvalidating parsers. High performance is achieved by integration across layers of software that are traditionally separate, by avoiding unnecessary data copying and transformation, and by careful attention to detail in the generated code. The effect of API design on XML performance is also briefly discussed. Categories and Subject Descriptors D.3.4 [Programming Languages]: Processors – code generation, compilers, optimization, parsing, retargetable compilers. D.2.8

We take the term X/O impedance mismatch to describe the difficulty of the OO paradigm to accommodate XML processing by means of recasting it to typed OO programming. In particular, given XML types (say, XML schemas), it is notoriously difficult to map them automatically to object types (say, object models) that (i) reasonably compare to native object types typically devised by OO developers; (ii) fully preserve the intent of the original XML types; (iii) fully support round-tripping of arbitrary, valid XML data; and (iv) provide a general and convenient programming model for XML data hosted by objects. We reveal the X/O impedance mismatch in particular detail. That is, we survey the relevant differences between XML and objects in terms of their data models and their type systems. In this process, we systematically record and assess Xto-O mapping options. Our illustrations employ XSD (1.0) as the XML-schema language of choice and C# (1.0–3.0) as the bound of OO language expressiveness.

XML is a standard format for data exchange and it is well suited to represent internet applications because of its textbased format. However, this flexibility means that it incurs higher data processing overhead than ordinary data formats. In this paper, we propose a high-performance XML processing method using a novel pattern recognition algorithm based on a grammar compression algorithm. In the method, training XML documents are pre-analyzed in order to detect frequently appearing constructs in the document. The extended XML parser uses the results of the pre-analysis to make its parsing faster with speculative input matching. The results of experiments show that the proposed method improves the performance of XML parsing by up to 182% (146 % on average) compared with an ordinary SAX parser with namespace processing under the condition that the target XML documents are similar to the pre-analyzed XML documents. 1.