Evaluating Performance and Quality of XML-Based Similarity Joins

Ribeiro, Leonardo Andrade; Härder, Theo

doi:10.1007/978-3-540-85713-6_18

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…We assume ordered trees where the sibling order matters. Ribeiro and Härder [2008] introduce extended pq-grams. Extended pq-grams consider, in addition to the structural similarity between trees, also the string similarity between leaf nodes.…”

Section: Related Workmentioning

confidence: 99%

The pq -gram distance between ordered labeled trees

Augsten

Böhlen

Gamper

2008

ACM Trans. Database Syst.

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When integrating data from autonomous sources, exact matches of data items that represent the same real-world object often fail due to a lack of common keys. Yet in many cases structural information is available and can be used to match such data. Typically the matching must be approximate since the representations in the sources differ.We propose pq-grams to approximately match hierarchical data from autonomous sources and define the pq-gram distance between ordered labeled trees as an effective and efficient approximation of the fanout weighted tree edit distance. We prove that the pq-gram distance is a lower bound of the fanout weighted tree edit distance and give a normalization of the pq-gram distance for which the triangle inequality holds. Experiments on synthetic and real-world data (residential addresses and XML) confirm the scalability of our approach and show the effectiveness of pq-grams. ACM Reference Format:Augsten, N., Böhlen, M., and Gamper, J. 2010. The pq-gram distance between ordered labeled trees.

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Section: Related Workmentioning

confidence: 99%

The pq -gram distance between ordered labeled trees

Augsten

Böhlen

Gamper

2008

ACM Trans. Database Syst.

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“…DBLP [6] stores bibliographic data in XML format and includes, among others, authors, titles, and venues of computer science publications. Due to its availability and intuitiveness, the DBLP dataset has been used in many works for experimental purposes, e.g., as a collection of sets [44, 45], as a collection of trees [37, 38, 46], as a large hierarchical document [34, 40], and as a coauthor network graph [42, 49]. In this section, we show the impact of differences in the data preparation process that converts raw DBLP XML data into the desired input format.…”

Section: A Link Is Not Enoughmentioning

confidence: 99%

“…According to the main author, the other datasets are proprietary. In the case of [45], the raw data (R) was provided by the authors. However, the input data could not be generated due to incomplete preparation instructions.…”

Section: Data Reproducibility In Similarity Joinsmentioning

confidence: 99%

A Link is not Enough – Reproducibility of Data

et al. 2019

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Although many works in the database community use open data in their experimental evaluation, repeating the empirical results of previous works remains a challenge. This holds true even if the source code or binaries of the tested algorithms are available. In this paper, we argue that providing access to the raw, original datasets is not enough. Real-world datasets are rarely processed without modification. Instead, the data is adapted to the needs of the experimental evaluation in the data preparation process. We showcase that the details of the data preparation process matter and subtle differences during data conversion can have a large impact on the outcome of runtime results. We introduce a data reproducibility model, identify three levels of data reproducibility, report about our own experience, and exemplify our best practices.

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“…2.1.2 Ordered trees. For ordered trees, we use epq-grams (Ribeiro and Härder, 2008), an extension of the concept of pq-grams (Augsten et al, 2010). Informally, all subtrees of a specific shape -as defined by the values of p and q -are called pq-grams of the corresponding tree.…”

Section: Textual Representationmentioning

confidence: 99%

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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Evaluating Performance and Quality of XML-Based Similarity Joins

Cited by 8 publications

References 10 publications

The pq -gram distance between ordered labeled trees

The pq -gram distance between ordered labeled trees

A Link is not Enough – Reproducibility of Data

Pushing similarity joins down to the storage layer in XML databases

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