APEX: an adaptive path index for XML data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Covering indexes for branching path queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Index Structures for Path Expressions
ICDT '99 Proceedings of the 7th International Conference on Database Theory
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
D(k)-index: an adaptive structural summary for graph-structured data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Exploiting Local Similarity for Indexing Paths in Graph-Structured Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Multiresolution Indexing of XML for Frequent Queries
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
XMark: a benchmark for XML data management
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Covering indexes for XML queries: bisimulation - simulation = negation
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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Answering structural queries of XML with index is an important approach of efficient XML query processing. Among existing structural indexes for XML data, F&B index is the smallest index that can answer all branching queries. However, an F&B index for less regular XML data often contains a large number of index nodes, and hence a large amount of main memory. If the F&B index cannot be accommodated in the available memory, its performance will degrade significantly. This issue has practically limited wider application of the F&B index. In this paper, we propose a disk organization method for the F&B index which shift part of the leave nodes in the F&B index to the disk and organize them judiciously on the disk. Our method is based on the observation that the majority of the nodes in a F&B index is often the leaf nodes, yet their access frequencies are not high. We select some leaves to output to disk. With the support of reasonable storage structure in main memory and in disk, we design efficient query processing method). We further optimize the design of the F&B index based on the query workload. Experimental results verified the effectiveness of our proposed approach.