Lore: a database management system for semistructured data
ACM SIGMOD Record
On supporting containment queries in relational database management systems
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
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
Holistic twig joins: optimal XML pattern matching
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Statistical synopses for graph-structured XML databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Discovering Structural Association of Semistructured Data
IEEE Transactions on Knowledge and Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Counting Twig Matches in a Tree
Proceedings of the 17th International Conference on Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Relational Databases for Querying XML Documents: Limitations and Opportunities
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Estimating the Selectivity of XML Path Expressions for Internet Scale Applications
Proceedings of the 27th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
D(k)-index: an adaptive structural summary for graph-structured data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Structural Joins: A Primitive for Efficient XML Query Pattern Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Exploiting Local Similarity for Indexing Paths in Graph-Structured Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
XPathLearner: an on-line self-tuning Markov histogram for XML path selectivity estimation
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient mining of XML query patterns for caching
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Holistic twig joins on indexed XML documents
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Maintenance of maximal frequent itemsets in large databases
Proceedings of the 2007 ACM symposium on Applied computing
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Principles of Holism for sequential twig pattern matching
The VLDB Journal — The International Journal on Very Large Data Bases
Effective pruning for XML structural match queries
Data & Knowledge Engineering
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Queries on semistructured data are hard to process due to the complex nature of the data and call for specialized techniques. Existing path-based indexes and query processing algorithms are not efficient for searching complex structures beyond simple paths, even when the queries are high-selective. We introduce the definition of minimal infrequent structures (MIS), which are structures that 1) exist in the data, 2) are not frequent with respect to a support threshold, and 3) all substructures of them are frequent. By indexing the occurrences of MIS, we can efficiently locate the high-selective substructures of a query, improving search performance significantly. An efficient data mining algorithm is proposed, which finds the minimal infrequent structures. Their occurrences in the XML data are then indexed by a lightweight data structure and used as a fast filter step in query evaluation. We validate the efficiency and applicability of our methods through experimentation on both synthetic and real data.