Graph Database Edge Prediction for Customer Support Ticket Relationship Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Customer support centers face challenges in efficiently managing high volumes of customer support tickets, particularly in identifying and retrieving relevant, related tickets for effective issue resolution, as existing methods rely on manual keyword searches that yield numerous records and lack real-time or historical relationship determination.
Innovation Solution
A database management system employs edge prediction on graphs to derive direct and predictive relationships between customer support tickets, generating a relationship data set that is communicated to user devices, prioritizing active tickets and clustering related nodes to provide a ranked or weighted list of relevant tickets to support engineers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual keyword searches are used to find related tickets, then support engineers can identify relevant tickets, but the process is time-consuming and yields numerous irrelevant records
Solution Approach 1:
The system pre-computes and stores relationship data between tickets in advance, including direct relationships (same customer, product, issue type) and predictive relationships (inferred through graph analysis). When a support engineer queries for related tickets, the pre-computed relationship dataset is immediately retrieved and filtered, eliminating the need for real-time analysis and providing instant results with high precision.
Solution Approach 2:
A graph database serves as an intermediary structure that models tickets as nodes and relationships as edges. The graph database management system maintains relationship datasets that mediate between the raw ticket data and the support engineer's query, enabling efficient traversal and retrieval of related tickets through predefined relationship types rather than brute-force searching.
2Reliability
If comprehensive ticket relationship analysis is performed, then accurate related tickets are identified, but system complexity increases
Solution Approach 1:
The relationship determination system is segmented into distinct, manageable components: (1) direct relationship determination based on explicit criteria (customer, product, issue type matching), (2) graph database construction that stores these relationships, and (3) predictive relationship analysis using graph algorithms. Each component has a specific function and can be independently optimized and maintained, reducing overall system complexity while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The system uses configurable parameters to control relationship determination, such as relationship thresholds, matching criteria weights, and graph traversal depth. These parameters can be adjusted to balance between comprehensive analysis and system complexity, allowing the system to adapt to different operational requirements without fundamental architectural changes.
3Productivity
If real-time relationship determination is implemented, then support efficiency improves, but computational resources are consumed
Solution Approach 1:
Relationship datasets are pre-computed and stored in the graph database when tickets are created or updated, rather than computing relationships on-demand during query operations. This shifts the computational workload to background processing times, enabling real-time query responses with minimal resource consumption during peak support operations.
Solution Approach 2:
The graph database maintains relationship datasets continuously updated as tickets are created, modified, or closed. This continuous maintenance ensures that relationship data is always current and ready for immediate retrieval, eliminating the need for repeated computational analysis while keeping the system synchronized with the latest ticket information.
Data Source
AI summary
A network device associated with a database management system receives information associated with a customer support ticket. Based on information in the database management system, a direct relationship between the received customer support ticket and a customer support ticket in the database may be determined. A graph including nodes representing customer support tickets is generated based on information in the database. Edge prediction is performed on the graph to derive relationships among the nodes in the graph. A predictive relationship between customer support tickets is derived. A relationship data set based on the direct relationship between the customer support tickets and based on the predictive relationship between the customer support tickets is generated. The relationship data set associated with the customer support ticket is communicated to the user device.