MDCCSG Content Metadata Identifier for Big Data Governance
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Solution Overview
Problem
Current content metadata identification methods are inadequate for efficiently sharing and governing big content data in a big data and pan-media environment, as they struggle with rich semantic description, authentication, and security, leading to difficulties in content management and governance.
Innovation Solution
A computer-implemented identification method using a uniform content metadata description framework and specification method, known as MDCCSG, which generates uniform identifiers with built-in authentication and security features, supporting efficient aggregation, distribution, and governance of big content data through a content-centric metadata identification system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If uniform resource locators (URLs) are employed to identify content resources, then resource location representation is achieved, but semantic description capability is insufficient
Solution Approach 1:
The content identifier is segmented into multiple functional components including content type identifier, content hash value, and metadata elements. This segmentation allows each component to serve specific purposes: the content type identifier provides semantic categorization, the hash value ensures unique identification and integrity verification, and metadata elements provide additional descriptive information. This resolves the contradiction by enabling rich semantic description without creating a monolithic complex identification system.
Solution Approach 2:
The content identifier structure is designed to be universal and multi-functional, serving simultaneously as a unique identifier, a semantic descriptor, an integrity verification mechanism, and a routing guide. The identifier can represent various content types (videos, images, texts, audio) while maintaining a unified structure, thereby providing rich semantic description capabilities without requiring separate identification systems for different content types.
2Adaptability or versatility
If Dublin Core metadata elements are used for content identification, then international standard compliance is achieved, but big content data sharing and governance requirements are not met
Solution Approach 1:
The metadata structure is designed to be dynamic and adaptable rather than static. The identifier can accommodate variable metadata elements depending on content type and specific requirements, allowing the system to adapt to diverse big content data sharing and governance scenarios. This dynamic structure enables the system to scale and evolve with changing requirements without being constrained by a fixed rigid framework.
Solution Approach 2:
Different metadata elements and identification strategies are applied locally according to specific content types and governance requirements. Rather than applying a uniform metadata structure to all content, the system allows customization of metadata elements based on local needs (e.g., different metadata for videos versus images), thereby achieving adaptability for big content data while maintaining manageable complexity through targeted rather than universal application.
3Reliability
If existing content identification methods are used, then basic content identification is achieved, but authentication and security guarantee capabilities are insufficient
Solution Approach 1:
Authentication and security verification functions are merged into the content identifier structure itself rather than being separate from it. The content hash value embedded in the identifier serves both as a unique content fingerprint and as a basis for integrity verification. Digital signatures and authentication mechanisms are integrated directly into the identifier generation and validation process, thereby enhancing reliability without requiring entirely separate authentication systems.
Solution Approach 2:
Authentication and security features are built into the identifier generation process from the beginning rather than being added as subsequent layers. The content hash is calculated and embedded during identifier creation, and authentication credentials are established in advance. This preliminary incorporation of security measures ensures that authentication and verification are inherent to the identification process itself, enhancing reliability while avoiding the complexity of layered additions.
4Productivity
If rich semantic description is implemented in content identifiers, then content searching and governance efficiency is improved, but identifier complexity increases
Solution Approach 1:
The identifier structure is segmented into distinct functional components: content type identifier for semantic categorization, content hash value for unique identification and integrity checking, and optional metadata elements for additional description. This segmentation allows the system to provide rich semantic description through targeted elements rather than requiring all elements to be complex, thereby improving searching and governance efficiency while managing overall identifier complexity through modular design.
Data Source
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AI summary
The present invention discloses an identification method of content metadata for cyber content sharing and governance (an MDCCSG identification method for short) and an application method of MDCCSG identifiers. The identification method is proposed to meet the requirements of big content data sharing and governance in a big data and pan-media environment, and is suitable for generating uniform identifiers for various types of content resources. The corresponding application method of MDCCSG identifiers can effectively support high-efficiency sharing and governance of big content data. The MDCCSG identification method includes three parts: a uniform description framework of content metadata, a uniform specification method for content metadata, and definition of core MDCCSG identifier elements. The identification method not only can describe rich semantic information of contents in details, but also has built-in capabilities of content trust authentication and security guarantee. Various content sharing and governance applications can be developed based on the MDCCSG identifiers, and many important functions for big content data can be effectively supported, including efficient aggregation and distribution, personalized active service, semantics-based deep analysis, authentication and registration, source tracing and responsibility investigation according to laws and the like.