Service Incident Graph Embeddings for Relationship Detection
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Solution Overview
Problem
Existing incident management systems struggle with efficiently detecting, diagnosing, and resolving service incidents due to the complexity of unstructured data and the lack of effective tools for systematically identifying relationships within large volumes of data, leading to slow resolution and inadequate institutional learning.
Innovation Solution
A system that models service incidents as contextual graphs using reduced vector embeddings, employing techniques like UMAP and PCA for dimensionality reduction, enabling intuitive visualization and clustering of incidents through interactive interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual investigation and information retrieval across disconnected systems is used, then flexibility in handling diverse incident types is maintained, but incident resolution speed decreases and institutional learning is insufficient
Solution Approach 1:
The patent merges multiple disconnected monitoring systems and incident data sources into a unified graph database structure. Incidents, alerts, logs, and metadata from various systems are consolidated into a single interconnected graph representation, enabling centralized investigation and faster resolution by eliminating the need to query multiple disconnected systems separately.
Solution Approach 2:
The patent introduces an intermediary layer in the form of a graph database that mediates between diverse incident data sources and users. This graph structure serves as a common representation that translates and connects data from different systems, allowing users to query and analyze incidents without needing to understand the complexity of underlying disconnected systems.
2Extent of automation
If natural language processing of incident records is used, then automated information extraction is improved, but the system becomes brittle and limited in identifying complex relationships
Solution Approach 1:
The patent replaces mechanical rule-based correlation systems with a graph-based semantic representation. Instead of using rigid NLP rules that are brittle and hard to maintain, the system uses a graph database to represent incident relationships semantically, enabling flexible and accurate identification of complex relationships through graph traversal and query languages like Cypher.
Solution Approach 2:
The patent changes the representation parameter from text-based NLP output to graph-structured data. Incidents are transformed from unstructured text records into graph nodes with explicit relationship edges, allowing the system to capture complex relationships through the graph topology rather than relying on the limitations of NLP processing.
3Productivity
If rules-based correlation of incidents is used, then systematic processing is improved, but the system fails to identify complex relationships within large volumes of unstructured data
Solution Approach 1:
The patent adds a dimensional transformation by representing incidents in a graph structure rather than processing them as flat text records. This graph dimension enables the system to visualize and traverse complex relationships between incidents, alerts, and metadata that would be difficult to detect in traditional tabular or unstructured data formats.
Solution Approach 2:
The patent creates a simplified graph representation that copies the essential relationships and structure of complex incident data into a more manageable format. This graph model preserves the semantic relationships while simplifying the data structure, making it easier to process and query compared to the original complex unstructured data.
Data Source
AI summary
A system and method for vector embedding of service incident data is described. In one aspect, a computer-implemented method comprising receiving service incident data includes free-form text data, structured metadata, and human-generated comments, constructing a graph representation of a service incident, the graph includes nodes representing the free-form text data, structured metadata, and human-generated comments of the service incident, and edges connecting related nodes, generating vector embeddings for the nodes and edges of the graph representation, applying dimensionality reduction to the vector embeddings to generate reduced embeddings, and storing the reduced embeddings and the vector embeddings in a database.