Abstract
Web query languages promise convenient and efficient access to Web data such as XML, RDF, or Topic Maps. Xcerpt is one such Web query language with strong emphasis on novel high-level constructs for effective and convenient query authoring, particularly tailored to versatile access to data in different Web formats such as XML or RDF. However, so far it lacks an efficient implementation to supplement the convenient language features. AMa χ oS is an abstract machine implementation for Xcerpt that aims at efficiency and ease of deployment. It strictly separates compilation and execution of queries: Queries are compiled once to abstract machine code that consists in (1) a code segment with instructions for evaluating each rule and (2) a hint segment that provides the abstract machine with optimization hints derived by the query compilation. This article summarizes the motivation and principles behind AMa χ oS and discusses how its current architecture realizes these principles.
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Bry, F., Furche, T., Linse, B. (2006). AMa χ oS—Abstract Machine for Xcerpt: Architecture and Principles. In: Alferes, J.J., Bailey, J., May, W., Schwertel, U. (eds) Principles and Practice of Semantic Web Reasoning. PPSWR 2006. Lecture Notes in Computer Science, vol 4187. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11853107_8
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DOI: https://doi.org/10.1007/11853107_8
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