Learn the key characteristics of semi-structured data and how it differs from structured and unstructured data. Prepare for the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
Which of the following statements most accurately describes semi-structured data?
A. Information that can be organized in rows and columns, like in a spreadsheet
B. Data that lacks any built-in organization or structure, such as images and texts
C. Data that combines features of both structured and unstructured data, using metadata for organization
D. Quantitative data processed and analyzed by conventional data tools
Answer
C. Data that combines features of both structured and unstructured data, using metadata for organization
Explanation
Semi-structured data is a type of data that falls between fully structured data (like data in a relational database) and completely unstructured data (like plain text or images). It has some organizational properties but does not conform to the strict tabular structure associated with relational databases.
Key characteristics of semi-structured data include:
- Presence of metadata: Semi-structured data contains tags or elements that provide information about the data itself. These metadata elements help to organize and describe the data.
- Flexible structure: Unlike structured data, semi-structured data does not adhere to a rigid schema. The structure can vary and evolve over time, allowing for more flexibility in data representation.
- Self-describing: The metadata in semi-structured data makes it self-describing, meaning that the data can be understood and processed without the need for a separate schema or data model.
Common examples of semi-structured data include XML, JSON, and NoSQL databases. These formats allow for the representation of complex, hierarchical data structures while still providing some level of organization through the use of metadata.
In summary, semi-structured data combines aspects of both structured and unstructured data, using metadata to provide organization and description while maintaining flexibility in its structure. Understanding the characteristics of semi-structured data is essential for working with modern data formats and systems in the field of artificial intelligence and data science.
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