Abstract
Previous works in change detection on XML documents are not suitable for detecting the changes to large XML documents as it requires a lot of memory to keep the two versions of XML documents in the memory. In this paper, we take a more conservative yet novel approach of using traditional relational database engines for detecting the changes to large unordered XML documents. We elaborate how we detect the changes on unordered XML documents by using relational database. To this end, we have implemented a prototype system called Xandy that converts XML documents into relational tuples and detects the changes from these tuples by using SQL queries. Our experimental results show that the relational approach has better scalability compared to published algorithms like X-Diff. The result quality of our approach is comparable to the one of X-Diff.
Keywords
- Execution Time
- Leaf Node
- Similarity Score
- Relational Database
- Internal Node
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Leonardi, E., Bhowmick, S.S., Madria, S. (2005). Xandy: Detecting Changes on Large Unordered XML Documents Using Relational Databases. In: Zhou, L., Ooi, B.C., Meng, X. (eds) Database Systems for Advanced Applications. DASFAA 2005. Lecture Notes in Computer Science, vol 3453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408079_65
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DOI: https://doi.org/10.1007/11408079_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25334-1
Online ISBN: 978-3-540-32005-0
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