Methods and Cost Models for XPath Query Processing in Main Memory Databases

Recent work on XPath evaluation has produced efficient relational index structures for maintaining and querying XML through a DBMS. Built on top of an relational encoding, named the XPath Accelerator, this thesis takes a closer look at its utilization within the scope of query processing. Basic XPath operations, such as axis steps and simple node tests, remain in the focus of the study. Appropriate database operations for their evaluation are introduced in the context of the main memory DBMS Monet. In those cases where the existing database operators fail to exploit the tree properties of XML data, new algorithms have been developed, designed specifically for evaluation of XPath axes. As an important step towards cost analysis for the proposed XPath operations, result size estimation is discussed in the trade off between accuracy and expense. Different methods show how statistical data as well as sampling techniques can be used for estimating result sizes of simple axis steps. The generation of cost functions mainly considers the time, that the XPath operations spend on data access. Even in main memory databases, CPU processing usually gets stalled for outstanding memory fetches. Therefore, our cost functions explicitly analyze the cache usage of the operations, adopting a hierarchical memory access model. Detailed tests demonstrate the accuracy and performance of the proposed result size and cost estimation techniques.