Knowledge Graph Mapping for IT Event and Configuration Item Correlation
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Solution Overview
Problem
Conventional data pipeline systems face challenges in effectively managing, analyzing, and deriving insights from diverse data types originating from various sources, including events, entities, and their intricate relationships, making it difficult to comprehend connections and extract meaningful patterns, which hinders comprehensive analytics, scalability, and adaptability to evolving business environments.
Innovation Solution
The system employs knowledge-based graphs to represent complex relationships and interconnected data, using graph databases like Neo4j to model event data with nodes and edges, enabling intuitive data analysis and real-time updates to create dynamic graphs that identify correlations and potential problematic CIs, facilitating proactive incident identification and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional data pipeline systems are used to manage diverse data types from various sources, then data collection and storage are achieved, but the ability to comprehend connections and extract meaningful patterns deteriorates
Solution Approach 1:
The patent introduces an intermediary knowledge graph layer between conventional data pipeline systems and analytical applications. This knowledge graph serves as a mediator that transforms diverse data from multiple sources into structured relationships, enabling connection understanding without increasing the complexity of individual data processing components. The knowledge graph absorbs the complexity of multi-source data integration while presenting simplified relationship queries to downstream systems.
2Loss of information
If comprehensive analytics are performed on diverse data types, then meaningful insights are extracted, but system scalability deteriorates
Solution Approach 1:
The patent segments the analytics function by separating data ingestion, knowledge graph construction, and analytical querying into distinct modular components. The knowledge graph itself is segmented into different data types and relationship categories, allowing comprehensive analytics to be performed on structured relationships while maintaining scalability through modular architecture. Each segment can be independently optimized and scaled according to specific analytical needs.
3Loss of time
If real-time data processing is implemented, then timely incident detection is achieved, but data processing complexity deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-processing and structuring data into the knowledge graph before incident detection is needed. Relationships and patterns are established in advance through knowledge graph construction, so that when incidents occur, the system can quickly query pre-organized relationship data rather than processing raw data in real-time. This shifts complexity from real-time processing to offline knowledge graph construction.
Data Source
AI summary
A computer-implemented method for managing information technology event data in a graph database, the method including: obtaining a graph database, the graph database including a set of nodes and set of edges that connect the set of nodes; receiving a first data, the first data object, the first data object and second data object representing information technology data; processing the first data object, wherein processing the first data object includes assigning a first identification field to the first data object and identifying additional fields of the first data object; determining the first data object does not exist as a node in the graph database; creating a first node for the first data object and assigning the first identification field to the first node; creating a first edge connecting the first data object to a first related node from the set of nodes of the graph database.