JsonCurer: Data Quality Management for JSON Based on an Aggregated Schema

High-quality data is critical to deriving useful and reliable information. However, real-world data often contains quality issues undermining the value of the derived information. Most existing research on data quality management focuses on tabular data, leaving semi-structured data under-exploited. Due to the schema-less and hierarchical features of semi-structured data, discovering and fixing quality issues is challenging and time-consuming. To address the challenge, this paper presents JsonCurer, an interactive visualization system to assist with data quality management in the context of JSON data. To have an overview of quality issues, we first construct a taxonomy based on interviews with data practitioners and a review of 119 real-world JSON files. Then we highlight a schema visualization that presents structural information, statistical features, and quality issues of JSON data. Based on a similarity-based aggregation technique, the visualization depicts the entire JSON data with a concise tree, where summary visualizations are given above each node, and quality issues are illustrated using Bubble Sets across nodes. We evaluate the effectiveness and usability of JsonCurer with two case studies. One is in the domain of data analysis while the other concerns quality assurance in MongoDB documents.