Inference of a Concise Regular Expression Considering Interleaving from XML Documents

Zhang, Xiaolan; Li, Yeting; Cui, Fanlin; Dong, Chunmei; Chen, Haiming

doi:10.1007/978-3-319-93037-4_31

Cited by 8 publications

(12 citation statements)

References 15 publications

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“…&(author + |editor + ) This suggests that we can get more accurate result by using subclasses with counting and interleaving. Actually there have been some preliminary work, e.g., [11,29,36]. This shows the necessity to further study new practical subclasses, based on the analyses and experiments of large-scale real data, which remains as a future work.…”

Section: New Practical Subclassesmentioning

confidence: 99%

Practical Study of Deterministic Regular Expressions from Large-scale XML and Schema Data

Chu

Mou

et al. 2018

Proceedings of the 22nd International Database Engineering &Amp; Applications Symposium on - IDEAS 2018

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Regular expressions are a fundamental concept in computer science and widely used in various applications. In this paper we focused on deterministic regular expressions (DREs). Considering that researchers didn't have large datasets as evidence before, we first harvested a large corpus of real data from the Web then conducted a practical study to investigate the usage of DREs. One feature of our work is that the data set is sufficiently large compared with previous work, which is obtained using several data collection strategies we proposed. The results show more than 98% of expressions in Relax NG are DRE, and more than 56% of expressions from RegExLib are DRE, while both Relax NG and RegExLib do not have the determinism constraint. These observations indicate that DREs are commonly used in practice. The results also show further study of subclasses of DREs is necessary. As far as we know, we are the first to analyze the determinism and the subclasses of DREs of Relax NG and RegExLib, and give these results. Furthermore, we give some discussions and applications of the data set. We obtain a DRE data set from the original data, which will be useful in practice and it has value in its own right. We find current research in new subclasses of DREs is insufficient, therefore it is necessary to do further study. We also analyze the referencing relationships among XSDs and define SchemaRank, which can be used in XML Schema design.

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Section: New Practical Subclassesmentioning

confidence: 99%

Practical Study of Deterministic Regular Expressions from Large-scale XML and Schema Data

Chu

Mou

et al. 2018

Proceedings of the 22nd International Database Engineering &Amp; Applications Symposium on - IDEAS 2018

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“…The presence of a schema for XML documents has many advantages, such as for query processing and optimization, data integration and exchange [11,30]. However, many XML documents in practice are not accompanied by a valid schema [16], making schema inference an attractive research topic [2,3,10,14,31]. Learning Relax NG schemas is an important research problem for schema inference, since it is more powerful than other XML schema languages, such as DTD or XSD [5] and has unrestricted supports for the interleaving operator.…”

Section: Introductionmentioning

confidence: 99%

“…Learning Relax NG schemas is an important research problem for schema inference, since it is more powerful than other XML schema languages, such as DTD or XSD [5] and has unrestricted supports for the interleaving operator. It is known that the essential task in Relax NG schema inference is learning RE(&) from a set of given sample [23,31].…”

Section: Introductionmentioning

confidence: 99%

Inferring Restricted Regular Expressions with Interleaving from Positive and Negative Samples

Chen

Zhang

et al. 2020

Advances in Knowledge Discovery and Data Mining

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The presence of a schema for XML documents has numerous advantages. Unfortunately, many XML documents in practice are not accompanied by a schema or a valid schema. Therefore, it is essential to devise algorithms to infer schemas. The fundamental task in XML schema inference is to learn regular expressions. In this paper, we focus on learning the subclass of RE(&) called SIREs (the subclass of regular expressions with interleaving). Previous work in this direction lacks inference algorithms that support inference from positive and negative examples. We provide an algorithm to learn SIREs from positive and negative examples based on genetic algorithms and parallel techniques. Our algorithm also has better expansibility, which means that our algorithm not only supports learning with positive and negative examples, but also supports learning with positive or negative examples only. Experimental results demonstrate the effectiveness of our algorithm.

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“…The presence of a schema for XML documents has many advantages, such as for query processing and optimization, development of database applications, data integration and exchange [15,42,34,18]. However, many XML documents in practice are not accompanied by a (valid) schema [3,37,36,6,41,25], making schema inference an attractive research problem [2,5,7,17,22,43,13,30,32]. Studying schema inference also has several practical motivations.…”

Section: Introductionmentioning

confidence: 99%

“…In such cases the interleaving is necessary. Here we list the more recent efforts on RE(&) inference (see [13,40,43,30,32]). The aim of these approaches is to infer restricted subclasses of single occurrence RE(&), in which each symbol occurs at most once, starting from a positive set of words.…”

Section: Introductionmentioning

confidence: 99%

An effective algorithm for learning single occurrence regular expressions with interleaving

Chen

Zhang

et al. 2019

Proceedings of the 23rd International Database Applications &Amp; Engineering Symposium on - IDEAS '19

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The advantages offered by the presence of a schema are numerous. However, many XML documents in practice are not accompanied by a (valid) schema, making schema inference an attractive research problem. The fundamental task in XML schema learning is inferring restricted subclasses of regular expressions. Most previous work either lacks support for interleaving or only has limited support for interleaving. In this paper, we first propose a new subclass Single Occurrence Regular Expressions with Interleaving (SOIRE), which has unrestricted support for interleaving. Then, based on single occurrence automaton and maximum independent set, we propose an algorithm iSOIRE to infer SOIREs. Finally, we further conduct a series of experiments on real datasets to evaluate the effectiveness of our work, comparing with both ongoing learning algorithms in academia and industrial tools in real-world. The results reveal the practicability of SOIRE and the effectiveness of iSOIRE, showing the high preciseness and conciseness of our work.

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Inference of a Concise Regular Expression Considering Interleaving from XML Documents

Cited by 8 publications

References 15 publications

Practical Study of Deterministic Regular Expressions from Large-scale XML and Schema Data

Practical Study of Deterministic Regular Expressions from Large-scale XML and Schema Data

Inferring Restricted Regular Expressions with Interleaving from Positive and Negative Samples

An effective algorithm for learning single occurrence regular expressions with interleaving

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