A number of XML labeling methods have been proposed to store XML documents in relational databases. However, they have a vulnerable point, in insertion operations. We propose the variable length endless insertable (VLEI) code and apply it to XML labeling to reduce the cost of insertion operations. Results of our experiments indicate that a combination of the VLEI code and Dewey order is effective for handling skewed insertions.

This paper proposes a new data-placement method named adaptive overlapped declustering, which can be applied to a parallel storage system using a value range partitioning-based distributed directory and primary-backup data replication, to improve the space utilization by balancing their access loads. The proposed method reduces data skews generated by data migration for balancing access load. While some data-placement methods capable of balancing access load or reducing data skew have been proposed, both requirements satisfied simultaneously. The proposed method also improves the reliability and availability of the system because it reduces recovery time for damaged backups after a disk failure. The method achieves this acceleration by reducing a large amount of network communications and disk I/O. Mathematical analysis shows the efficiency of space utilization under skewed access workloads. Queuing simulations demonstrated that the proposed method halves backup restoration time, compared with the traditional chained declustering method.