Structured & Hierarchical Data

Overview

Unlike tabular data, structured data formats like JSON and XML allow for complex, nested relationships between elements. These formats are essential for representing objects, configurations, and web-based data exchanges. This section provides tools to navigate and validate these hierarchical structures.

Key Objectives:

  1. Hierarchy Visualization: We map the nested structure of the files to understand the depth and complexity of the data.
  2. Schema Validation: We check if the files conform to a standard structure or if they vary wildly within the dataset.
  3. Metadata Extraction: We identify key keys, tags, and attributes that define the data’s content.

Supported Formats

JSON (.json)

JavaScript Object Notation (JSON) is the de facto standard for modern web APIs and NoSQL databases. - Our Approach: We flatten the nested structure to create a readable summary of keys and data types, making it easier to audit large JSON collections.

XML (.xml)

Extensible Markup Language (XML) is a robust, mature standard used heavily in document storage, scientific metadata, and enterprise systems. - Our Approach: We parse the document tree to identify the root element, count child nodes, and extract attributes. We also check for well-formedness to ensure the files can be processed by standard parsers.

Common Curation Challenges

  • Deep Nesting: excessively deep hierarchies can make data difficult to query and analyze. We flag files with unusual depth.
  • Inconsistent Schemas: In flexible formats like JSON, different records may have different fields. Our tools help identify these inconsistencies.
  • Syntax Errors: A single missing bracket or tag can render an entire file unreadable. We verify the basic syntax integrity of every file.