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Efficient Top-k Approximate Subtree Matching in Small Memory
August 2011 (vol. 23 no. 8)
pp. 1123-1137
ASCII Text
x 
 
Nikolaus Augsten, Denilson Barbosa, Michael M. Böhlen, Themis Palpanas, "Efficient Top-k Approximate Subtree Matching in Small Memory," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 8, pp. 1123-1137, August, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2010.245,
author = {Nikolaus Augsten and Denilson Barbosa and Michael M. Böhlen and Themis Palpanas},
title = {Efficient Top-k Approximate Subtree Matching in Small Memory},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {23},
number = {8},
issn = {1041-4347},
year = {2011},
pages = {1123-1137},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.245},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Efficient Top-k Approximate Subtree Matching in Small Memory
IS - 8
SN - 1041-4347
SP1123
EP1137
EPD - 1123-1137
A1 - Nikolaus Augsten,
A1 - Denilson Barbosa,
A1 - Michael M. Böhlen,
A1 - Themis Palpanas,
PY - 2011
KW - Approximate subtree matching
KW - tree edit distance
KW - top-k queries
KW - XML
KW - subtree pruning
KW - similarity search.
VL - 23
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Nikolaus Augsten, Free University of Bozen-Bolzano, Bozen
Denilson Barbosa, University of Alberta, Edmonton
Michael M. Böhlen, University of Zurich, Zurich
Themis Palpanas, University of Trento, Trento
We consider the Top-k Approximate Subtree Matching (tasm) problem: finding the k best matches of a small query tree within a large document tree using the canonical tree edit distance as a similarity measure between subtrees. Evaluating the tree edit distance for large XML trees is difficult: the best known algorithms have cubic runtime and quadratic space complexity, and, thus, do not scale. Our solution is tasm-postorder, a memory-efficient and scalable tasm algorithm. We prove an upper bound for the maximum subtree size for which the tree edit distance needs to be evaluated. The upper bound depends on the query and is independent of the document size and structure. A core problem is to efficiently prune subtrees that are above this size threshold. We develop an algorithm based on the prefix ring buffer that allows us to prune all subtrees above the threshold in a single postorder scan of the document. The size of the prefix ring buffer is linear in the threshold. As a result, the space complexity of tasm-postorder depends only on k and the query size, and the runtime of tasm-postorder is linear in the size of the document. Our experimental evaluation on large synthetic and real XML documents confirms our analytic results.

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Index Terms:
Approximate subtree matching, tree edit distance, top-k queries, XML, subtree pruning, similarity search.
Citation:
Nikolaus Augsten, Denilson Barbosa, Michael M. Böhlen, Themis Palpanas, "Efficient Top-k Approximate Subtree Matching in Small Memory," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 8, pp. 1123-1137, Aug. 2011, doi:10.1109/TKDE.2010.245
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