Practical Study of Subclasses of Regular Expressions in DTD and XML Schema

Li, Yeting; Zhang, Xiaolan; Peng, Fei; Chen, Haiming

doi:10.1007/978-3-319-45817-5_29

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…In terms of preciseness and conciseness, our work has achieved satisfying results compared with existing methods, reaching higher preciseness with less description length. Specifically, indicators Language Size (|L(r)|) [5] and datacost (DC) [5] are used to measure preciseness, while Len [30] and Nesting Depth (ND) [31] for conciseness. Similar as the discussion of |L(r)| and Len above, we have that larger the value of DC (ND) is, more precise (concise) the regular expression will be.…”

Section: Methodsmentioning

confidence: 99%

“…Most existing subclasses of regular expressions for XML are defined on standard regular expressions, e.g., [5,7,6,16,35] which were analyzed together in [31,28]. For single occurrence regular expressions (SOREs), in which each symbol occurs at most once and its subclass chain regular expressions (CHAREs), Bex et al proposed two inference algorithms RWR and CRX [7,8].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

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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“…Finally, we collected 273,088 schemas files in total, the amount of which is 36 times of Grijzenhout et al [18], 34 times of Björklund et al [10] and 37 times of Li et al [24]. In details, DTD files are expanded by 39 times compared with Li et al [24] and XSD files expanded by 15 times compared Björklund et al [10] and Relax NGs expanded to 41 times of Grijzenhout et al [18]. The details are shown in Table 1.…”

Section: Harvestingmentioning

confidence: 99%

“…In practice many DTDs and XSDs contains subclasses of DREs so we study the usages of various subclasses of DREs. We use the definitions of the subclasses of restricted REs from [24]. Existing subclasses of DREs are all defined on standard REs, i,e., SORE [7], Simplified CHARE [7], eSimplified CHARE [24].…”

Section: Usage Of Subclasses Of Dresmentioning

confidence: 99%

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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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Learning Restricted Regular Expressions with Interleaving from XML Data

Zhang

Han

et al. 2018

Conceptual Modeling

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Practical Study of Subclasses of Regular Expressions in DTD and XML Schema

Cited by 8 publications

References 20 publications

An effective algorithm for learning single occurrence regular expressions with interleaving

An effective algorithm for learning single occurrence regular expressions with interleaving

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

Learning Restricted Regular Expressions with Interleaving from XML Data

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