NAIST-IS-DD0661209 Doctoral Dissertation Making XML Database Systems Scalable to Computer Resources and Data Volumes

Increasing use of XML has emphasized the need for scalable database systems that are capable of handling a large amount of XML data efficiently. This study explores ef-fective methods for making a scalable XML database system in the following aspects: (a) scalability to data volumes, (b) scalable XML processing with a shared-nothing PC cluster, and (c) scalable database processing on shared-memory multiprocessors. In the study of (a), we propose an XQuery processing scheme in which an XML document is internally represented as a set of blocks and can directly be stored on a secondary storage. Our experimental results showed that our storage scheme is scalable to data volumes and outperforms competing schemes with respect to I/O intensive workloads. In (b), we discuss on-the-fly XML processing using shared-nothing PC clusters. We propose a scheme for distributed and parallel query processing that employs a pass-by-reference semantics by using remote proxy. Previously proposed methods that use pass-by-value semantics have often suffered from redundant communication between processor elements and limited inter-operator parallelism. To cope with these prob