Landscape Graph for IT Operations Using Trigger Code
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Solution Overview
Problem
Current systems fail to provide a usable graph for IT landscape operations, lacking the ability to integrate information from distributed domains for software deployment, versions, integrations, high availability, disaster setup, hardware utilization, and instance assignment to consumers.
Innovation Solution
A landscape graph is created using a computer-implemented method that includes a trigger generator module to generate trigger code for graph business objects, allowing for automatic creation and synchronization of graph database objects, enabling graph traversal and query flexibility, and synchronizing with relational databases to replicate changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data objects are stored in distributed relational databases, then data can be persisted and managed in separate domains, but it becomes difficult to provide a unified graph representation for cross-domain data analysis
Solution Approach 1:
The patent introduces a graph database as an intermediary layer between distributed relational databases. This graph database receives replicated data objects and relationships from multiple relational databases, transforming them into a unified graph structure that enables cross-domain analysis while keeping the original distributed storage architecture intact.
Solution Approach 2:
The patent creates copies of data objects and their relationships from relational databases into a graph database structure. This copying process allows the same data to be accessed in both relational and graph formats, enabling cross-domain analysis without modifying the original distributed storage system.
2Reliability
If manual synchronization is used between relational databases and graph database, then data consistency can be maintained, but it results in loss of time and reduced productivity
Solution Approach 1:
The patent implements automated synchronization mechanisms where the system autonomously replicates data objects and relationships from relational databases to the graph database without manual intervention. This self-service approach maintains data consistency while eliminating the time loss associated with manual synchronization processes.
Solution Approach 2:
The patent establishes continuous automated synchronization processes that continuously replicate data changes from relational databases to the graph database. This continuous action ensures data consistency is maintained without periodic manual intervention, reducing time loss and improving productivity.
3Loss of information
If comprehensive data replication is performed from relational databases to graph database, then complete graph representation is achieved, but it increases device complexity and resource consumption
Solution Approach 1:
The patent applies selective replication strategies where not all data objects and relationships are replicated uniformly. Instead, the system identifies and replicates only those data objects and relationships that are relevant for graph analysis, reducing the complexity of the replication system while maintaining data completeness for analytical purposes.
Data Source
AI summary
In an implementation, a trigger generator module of a graph business object (GBO) factory creates trigger code. After replicating changes to an active business graph, the GBO factory: 1) for a GBO/graph business relation (GBR) create or delete action, executing trigger code of a specified trigger for a respective GBO/GBR type or 2) for a GBO/GBR update action of GBO/GBR attributes, executing trigger code of a specified trigger for a respective GBO/GBR type and attribute. Creating a temporary recommendation node (TRN) is created with the trigger code and using the GBO factory.