A cluster-based approach to XML similarity joins

Ribeiro, Leonardo Andrade; Härder, Theo; Pimenta, Fernanda

doi:10.1145/1620432.1620451

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…where ␤ Յ 0 is a decay rate constant (Ribeiro et al, 2009). The intuition behind LWS is that in tree-structured XML data, more general concepts are normally placed at higher nesting depths.…”

Section: Xml Databasesmentioning

confidence: 99%

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Pushing similarity joins down to the storage layer in XML databases

Ribeiro

Härder

2017

IJWIS

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Purpose This article aims to explore how to incorporate similarity joins into XML database management systems (XDBMSs). The authors aim to provide seamless and efficient integration of similarity joins on tree-structured data into an XDBMS architecture. Design/methodology/approach The authors exploit XDBMS-specific features to efficiently generate XML tree representations for similarity matching. In particular, the authors push down a large part of the structural similarity evaluation close to the storage layer. Findings Empirical experiments were conducted to measure and compare accuracy, performance and scalability of the tree similarity join using different similarity functions and on the top of different storage models. The results show that the authors’ proposal delivers performance and scalability without hurting the accuracy. Originality/value Similarity join is a fundamental operation for data integration. Unfortunately, none of the XDBMS architectures proposed so far provides an efficient support for this operation. Evaluating similarity joins on XML is challenging, because it requires similarity matching on the text and structure. In this work, the authors integrate similarity joins into an XDBMS. To the best of the authors’ knowledge, this work is the first to leverage the storage scheme of an XDBMS to support XML similarity join processing.

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Section: Xml Databasesmentioning

confidence: 99%

“…Next, we briefly review an approach based on path clustering, which provides the basis for tackling all the issues mentioned above. For a detailed discussion, please see Ribeiro et al (2009). IJWIS 13,1…”

Section: Xml Path Clusteringmentioning

confidence: 99%

Pushing similarity joins down to the storage layer in XML databases

Ribeiro

Härder

2017

IJWIS

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“…Ribeiro et al [30] present a distance for data-centric XML that considers both the tree structure and the text values of the leaf nodes. The paths of all trees in a dataset are clustered and the structure of a tree is represented by the set of cluster identifiers in which its paths participate.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2011

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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 pseudometric 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. 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 datacentric 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-gram 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 ...

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Ingredients for Accurate, Fast, and Robust XML Similarity Joins

Ribeiro

Härder

2011

Lecture Notes in Computer Science

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A cluster-based approach to XML similarity joins

Cited by 5 publications

References 31 publications

Pushing similarity joins down to the storage layer in XML databases

Pushing similarity joins down to the storage layer in XML databases

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

Ingredients for Accurate, Fast, and Robust XML Similarity Joins

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