Content Server Graph Analysis for Digital Content Market
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Solution Overview
Problem
Existing graph generation devices for content analysis are limited in their usability for purposes beyond simple character relationship analysis, as they primarily use single node labels for characters and lack flexibility for analysis of multiple factors.
Innovation Solution
A content server and system that generates a graph for analyzing the relationship between multiple factors, including members, components produced by members, digital content items, and sales data, by using a content communication device and content control device to display a graph with multiple nodes and edges representing different entities and their relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a simple character-relationship graph with single node labels is used, then the graph generation is simple and quick, but the graph is less easily usable for other purposes such as analysis
Solution Approach 1:
The graph structure is enhanced to serve multiple functions: it not only shows character relationships but also enables various types of analysis including sales analysis, component analysis, and member contribution analysis. The nodes and edges are designed to represent multiple entities and relationships simultaneously, making the graph versatile for different analytical purposes.
Solution Approach 2:
The graph is segmented into multiple types of nodes (representing different entities such as characters, components, members, sales data) and edges (representing different relationships). This segmentation allows each node and edge to have specific meanings and properties, enabling detailed analysis while maintaining an organized structure that doesn't compromise generation simplicity.
2Adaptability or versatility
If a graph with multiple nodes and edges representing different entities is generated, then the analysis capability is improved, but the graph complexity increases
Solution Approach 1:
The graph introduces additional dimensions by incorporating multiple attributes and properties for nodes and edges beyond simple labels. Nodes represent different entities with various properties, and edges represent different relationship types. This dimensional enrichment enables comprehensive analysis without requiring completely separate graphs for each analysis type, thus managing complexity through structured multi-dimensionality.
Solution Approach 2:
The graph structure is designed to be dynamic and adaptable, allowing nodes and edges to represent different entities and relationships based on the data being analyzed. The same graph framework can flexibly represent character relationships, sales data, component information, or member contributions depending on what is being analyzed, reducing the need for multiple fixed-structure graphs.
Data Source
AI summary
A content server (250) for managing a digital content item (80) sold in a content market (65) includes a content communication device (52) that communicates with a member terminal (40) including a display (40a) and a content control device (251) that causes the display (40a) to display an analytical graph (201a) through the content communication device (52). The analytical graph (201a) includes two or more nodes (202) representing different entities extracted from sales data and contract information and an edge (203) representing a relationship between the two or more nodes (202). The analytical graph (201a) includes the edge (203) extending from a node (202), as a center, representing identification information of the digital content item (80) to a node (202) representing an entity different from an entity for the digital content item (80).