Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Relational Databases for Querying XML Documents: Limitations and Opportunities
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Recursive XML Schemas, Recursive XML Queries, and Relational Storage: XML-to-SQL Query Translation
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
XANDY: detecting changes on large unordered XML documents using relationalDatabases
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
XML structural delta mining: issues and challenges
Data & Knowledge Engineering - Special issue: ER 2003
DTD-Diff: A change detection algorithm for DTDs
Data & Knowledge Engineering
Splitter: a proxy-based approach for post-migration testing of web applications
Proceedings of the 5th European conference on Computer systems
DTD-Diff: a change detection algorithm for DTDs
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
OXONE: a scalable solution for detecting superior quality deltas on ordered large xml documents
ER'06 Proceedings of the 25th international conference on Conceptual Modeling
E-Metadata versioning system for data warehouse schema
International Journal of Metadata, Semantics and Ontologies
E-Metadata versioning system for data warehouse schema
International Journal of Metadata, Semantics and Ontologies
Temporal and multi-versioned XML documents: A survey
Information Processing and Management: an International Journal
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Several relational approaches have been proposed to detect the changes to XML documents by using relational databases. These approaches store the XML documents in the relational database and issue SQL queries (whenever appropriate) to detect the changes. All of these relational-based approaches use the schema-oblivious XML storage strategy for detecting the changes. However, there is growing evidence that schema-conscious storage approaches perform significantly better than schema-oblivious approaches as far as XML query processing is concerned. In this paper, we study a relational-based unordered XML change detection technique (called HELIOS) that uses a schema-conscious approach (Shared-Inlining) as the underlying storage strategy. HELIOS is up to 52 times faster than X-Diff [7] for large datasets (more than 1000 nodes). It is also up to 6.7 times faster than XANDY [4]. The result quality of deltas detected by HELIOS is comparable to the result quality of deltas detected by XANDY.