As the popularity of areas including document storage and distributed systems continues to grow, the demand for high performance XML databases is increasingly evident. This has led to a number of research efforts aimed at exploiting the maturity of relational database systems in order to increase XML query performance. In our approach, we use an index structure based on a metamodel for XML databases combined with relational database technology to facilitate fast access to XML document elements. The query process involves transforming XPath expressions to SQL which can be executed over our optimised query engine. As there are many different types of XPath queries, varying processing logic may be applied to boost performance not only to individual XPath axes, but across multiple axes simultaneously. This paper describes a pattern based approach to XPath query processing, which permits the execution of a group of XPath location steps in parallel.
Abstract. XML query optimization continues to occupy considerable research effort due to the increasing usage of XML data. Despite many innovations over recent years, XML databases struggle to compete with more traditional database systems. Rather than using node indexes, some efforts have begun to focus on creating partitions of nodes within indexes. The motivation is to quickly eliminate large sections of the XML tree based on the partition they occupy. In this research, we present one such partition index that is unlike current approaches in how it determines size and number of these partitions. Furthermore, we provide a process for compacting the index and reducing the number of node access operations in order to optimize XML queries.
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