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Related Concept Videos

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Schemas

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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Maximum Size of Aggregate01:12

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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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Data: Types and Distribution01:19

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Related Experiment Video

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

Kai Xiong, Xinyi Xu, Siwei Fu

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    |April 16, 2024
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    Summary
    This summary is machine-generated.

    Managing data quality in JSON is challenging. JsonCurer is an interactive visualization system designed to help identify and fix data quality issues in semi-structured JSON data, improving information reliability.

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    Area of Science:

    • Computer Science
    • Data Management
    • Information Visualization

    Background:

    • High-quality data is essential for reliable information, but real-world data often suffers from quality issues.
    • Existing data quality research predominantly addresses tabular data, neglecting semi-structured formats like JSON.
    • The schema-less and hierarchical nature of JSON data complicates the discovery and resolution of quality problems.

    Purpose of the Study:

    • To introduce JsonCurer, an interactive visualization system for managing data quality in JSON data.
    • To address the challenges of identifying and rectifying quality issues in semi-structured data.
    • To provide data practitioners with effective tools for ensuring the reliability of JSON datasets.

    Main Methods:

    • Developed a taxonomy of data quality issues based on practitioner interviews and analysis of 119 JSON files.
    • Designed a schema visualization to present structural information, statistical features, and quality issues.
    • Employed a similarity-based aggregation technique to create a concise tree visualization of JSON data, incorporating summary visualizations and Bubble Sets for quality issue illustration.

    Main Results:

    • JsonCurer effectively visualizes structural information, statistical features, and data quality issues within JSON data.
    • The system utilizes a concise tree structure with aggregated nodes and Bubble Sets to represent complex data and quality problems.
    • Case studies in data analysis and MongoDB quality assurance demonstrated the effectiveness and usability of JsonCurer.

    Conclusions:

    • JsonCurer offers a novel approach to data quality management for JSON data.
    • The interactive visualization system aids in the efficient discovery and remediation of data quality issues.
    • JsonCurer enhances the reliability of information derived from semi-structured data, particularly in data analysis and database quality assurance contexts.