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 very 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, little attention has been paid to structural errors.#R##N##R##N#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.
['Shanshan Ying', 'Flip Korn', 'Barna Saha', 'Divesh Srivastava']
TreeScope: finding structural anomalies in semi-structured data