Windowed pq -grams for approximate joins of data-centric XML

Windowed pq -grams for approximate joins of data-centric XML In data integration applications, a join matches elements that are common to two data sources. Since elements are represented slightly different in each source, an approximate join must be used to do the matching. For XML data, most existing approximate join strategies are based on some ordered tree matching technique, such as the tree edit distance. In data-centric XML, however, the sibling order is irrelevant, and two elements should match even if their subelement order varies. Thus, approximate joins for data-centric XML must leverage unordered tree matching techniques. This is computationally hard since the algorithms cannot rely on a predefined sibling order. In this paper, we give a solution for approximate joins based on unordered tree matching. The core of our solution are windowed pq-grams which are small subtrees of a specific shape. We develop an efficient technique to generate windowed pq -grams in a three-step process: sort the tree, extend the sorted tree with dummy nodes, and decompose the extended tree into windowed pq -grams. The windowed pq -grams distance between two trees is the number of pq -grams that are in one tree decomposition only. We show that our distance is a pseudo-metric and empirically demonstrate that it effectively approximates the unordered tree edit distance. The approximate join using windowed pq -grams can be efficiently implemented as an equality join on strings, which avoids the costly computation of the distance between every pair of input trees. Experiments with synthetic and real world data confirm the analytic results and show the effectiveness and efficiency of our technique. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Windowed pq -grams for approximate joins of data-centric XML

Loading next page...
 
/lp/springer_journal/windowed-pq-grams-for-approximate-joins-of-data-centric-xml-a0CdBuEm9a
Publisher
Springer-Verlag
Copyright
Copyright © 2012 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-011-0254-6
Publisher site
See Article on Publisher Site

Abstract

In data integration applications, a join matches elements that are common to two data sources. Since elements are represented slightly different in each source, an approximate join must be used to do the matching. For XML data, most existing approximate join strategies are based on some ordered tree matching technique, such as the tree edit distance. In data-centric XML, however, the sibling order is irrelevant, and two elements should match even if their subelement order varies. Thus, approximate joins for data-centric XML must leverage unordered tree matching techniques. This is computationally hard since the algorithms cannot rely on a predefined sibling order. In this paper, we give a solution for approximate joins based on unordered tree matching. The core of our solution are windowed pq-grams which are small subtrees of a specific shape. We develop an efficient technique to generate windowed pq -grams in a three-step process: sort the tree, extend the sorted tree with dummy nodes, and decompose the extended tree into windowed pq -grams. The windowed pq -grams distance between two trees is the number of pq -grams that are in one tree decomposition only. We show that our distance is a pseudo-metric and empirically demonstrate that it effectively approximates the unordered tree edit distance. The approximate join using windowed pq -grams can be efficiently implemented as an equality join on strings, which avoids the costly computation of the distance between every pair of input trees. Experiments with synthetic and real world data confirm the analytic results and show the effectiveness and efficiency of our technique.

Journal

The VLDB JournalSpringer Journals

Published: Aug 1, 2012

References

  • The tree-to-tree correction problem
    Tai, K.-C.
  • XML stream processing using tree-edit distance embeddings
    Garofalakis, M.; Kumar, A.
  • A methodology for clustering XML documents by structure
    Dalamagas, T.; Cheng, T.; Winkel, K.-J.; Sellis, T.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off