Data Object Clusters for Semantic Relationship Management
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Solution Overview
Problem
In complex data systems, such as those used in business operations, it is challenging to organize and identify related data objects due to a lack of clear relationships, leading to difficulties in replication, API implementation, deployment, and metadata management, which can result in errors and inefficiencies.
Innovation Solution
The concept of data object clusters is introduced, where an anchor data object with a semantic context is used to group related data objects, allowing for easier identification and management of relationships, thereby simplifying replication, API creation, and deployment tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data objects are organized without clear relationships, then the system can handle more data objects, but the difficulty of identifying and managing relationships increases
Solution Approach 1:
The patent segments the complex data object system into clusters, where each cluster is a manageable group of related data objects. This segmentation allows the system to handle large quantities of data objects while maintaining organization through hierarchical grouping, thereby reducing the difficulty of identifying relationships among individual objects.
Solution Approach 2:
The patent introduces cluster definitions as intermediary structures that mediate between individual data objects and the overall system. These cluster definitions serve as organizers that explicitly define relationships between data objects, making it easier to identify and manage connections without requiring direct analysis of every object pair.
2Ease of operation
If data objects are manually organized with clear relationships, then relationship identification becomes easier, but the time and resources required for organization increase
Solution Approach 1:
The patent performs preliminary organization by automatically creating cluster definitions that group data objects based on their relationships. This preliminary action establishes the organizational structure in advance, making subsequent relationship identification easier without requiring manual intervention during operations, thus reducing time loss.
Solution Approach 2:
The system enables self-service organization through automatic cluster definition generation. The system autonomously analyzes data object relationships and creates appropriate cluster structures without manual input, thereby achieving easy relationship identification while minimizing the time and resources required for organization.
3Productivity
If cluster definitions are created for all data objects, then management efficiency improves, but the complexity of the system increases
Solution Approach 1:
The patent creates a universal cluster definition structure that can accommodate multiple types of data objects and relationships through a single standardized framework. This multi-functional approach allows the system to manage diverse data objects efficiently without requiring separate organizational mechanisms for each type, thereby improving productivity while controlling complexity through standardization.
Data Source
AI summary
Techniques and solutions for defining clusters of data objects are provided. An anchor data object for the cluster is determined. The anchor data object is associated with a semantic concept. Other data objects included in the cluster are also associated with the semantic concept. One or more data objects that are related to the anchor data object are added to the cluster. Additional data objects, related to the one or more other data objects, or to other data objects of the additional data objects, are added to the cluster. The cluster is associated with a name, which can be used to identify data objects that are part of the cluster. The cluster can be used for a variety of purposes, including defining a replication task, for the creation of an application program interface, or for defining a deployment task that deploys at least a portion of cluster data objects.