Structural inference for semistructured data

Sankey, Jason; Wong, Raymond K.

doi:10.1145/502585.502613

Cited by 9 publications

(1 citation statement)

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“…The most concise one is selected as the best answer. [8] uses multiple approaches to generate probabilistic string automata representing regular expressions, by application of inductive inference theory. Geert et al [2] propose to infer a concise DTD from the XML data.…”

Section: Related Workmentioning

confidence: 99%

TreeScope

et al. 2015

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Semi-structured data are prevalent on the web, with formats such as XML and JSON soaring in popularity due to their generality, flexibility and easy customization. However, these same features make semi-structured data prone to a range of data quality errors, from errors in content to errors in structure. While the former has been well studied, not much attention has been paid to structural errors, which can impact applications quite severely.In this demonstration, we present TREESCOPE, which analyzes semi-structured data sets with the goal of automatically identifying structural anomalies from the data. Our techniques learn robust structural models that have high support, to identify potential errors in the structure. Identified structural anomalies are then concisely summarized to provide plausible explanations of the potential errors. The goal of this demonstration is to enable an interactive exploration of the process of identifying and summarizing structural anomalies in semi-structured data sets.

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Section: Related Workmentioning

confidence: 99%