Market Graph Database for Industrial Relationship Mapping
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Solution Overview
Problem
Traditional market data structures struggle to establish complex relationships between companies, such as supplier/customer or subsidiary relationships, due to their limited logical structure, making it difficult to perform deep analysis of intercompany relationships.
Innovation Solution
A market graph data structure is introduced, which represents relationships between companies using nodes and edges, allowing for self-referential analysis and dynamic generation of complex datasets to identify relationships like competitors, customers, suppliers, and subsidiaries by analyzing various data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional market data structures (tables) are used to store company information, then the data storage is simple and straightforward, but the ability to establish complex relationships between companies (supplier/customer, subsidiary relationships) is limited
Solution Approach 1:
The patent transitions from traditional two-dimensional tabular data structures to a multi-dimensional graph data structure. Nodes represent companies while edges represent various relationship types (competitor, customer, supplier, subsidiary). This dimensional change enables the representation of complex intercompany relationships that cannot be captured in flat tables, directly resolving the contradiction between relationship adaptability and structural simplicity.
2Loss of information
If field similarities are used to identify relationships between companies, then the identification process is simple, but the depth and complexity of relationship analysis is insufficient
Solution Approach 1:
The graph data structure enables feedback mechanisms where relationships between companies are continuously discovered and refined. The system can traverse the graph to identify indirect relationships (e.g., a company's customer's customer), and use this information to refine relationship classifications. This feedback loop allows for progressive deepening of relationship analysis without requiring complete re-analysis from scratch.
Solution Approach 2:
The patent implements nested relationship structures where companies can have multiple levels of relationships. For example, a subsidiary relationship can contain further nested subsidiary relationships, and a customer relationship can lead to identifying the customer's suppliers. This nesting capability allows the system to capture complex multi-level intercompany relationships while maintaining a structured approach to detection.
3Productivity
If traditional data structures are used for market analysis, then the analysis scope is limited to dimensional aspects, but the ability to perform deep intercompany relationship analysis is hindered
Solution Approach 1:
The graph data structure serves multiple analytical functions simultaneously. It can identify direct and indirect relationships, trace supply chains, map competitive landscapes, and analyze corporate structures. This multi-functionality enables comprehensive intercompany relationship analysis within a single unified data structure, significantly enhancing analytical productivity compared to traditional specialized approaches for each analysis type.
Data Source
AI summary
Systems and techniques for semantic graph database capture of industrial organization and market structure are described herein. Electronic market data may be obtained from an electronic data source. A set of entities and corresponding relationships may be extracted from the electronic market data. A property may be determined for each of the relationships. A market graph data structure may be generated using the set of entities. A search query may be received including an identifier of a member of the set of entities. A market graphic may be generated based on a node corresponding to the identifier and a set of edges corresponding to the node. The market graphic may be output in a computer-readable format.