Automated Workflow Generation for Knowledge Graph Governance
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Solution Overview
Problem
Conventional approaches are inadequate for efficiently managing and governing large datasets associated with knowledge graphs and data catalogs, particularly due to the complexity and scale of these datasets, which often exceed one trillion facts or triples.
Innovation Solution
A computing platform is configured to generate and implement automated workflows based on process models, enabling the automation of data governance and enrichment of datasets, including graph-based data arrangements, without the need for manual intervention or specialized knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual workflow development is used, then specialized knowledge and control are improved, but cycle time and cost increase while autonomy decreases
Solution Approach 1:
The system performs preliminary action by automatically generating workflow process models before manual development begins. The automated workflow generator creates initial process models using AI/ML techniques, which then serve as starting points for developer refinement, thereby reducing the time required while maintaining quality control.
Solution Approach 2:
An automated workflow generator acts as an intermediary between business requirements and final workflow implementations. This intermediary component uses AI/ML to translate high-level process definitions into detailed process models, reducing direct developer involvement for routine tasks while maintaining overall system control.
2Reliability
If manual workflow development is used, then specialized knowledge is improved, but cost increases and autonomy decreases
Solution Approach 1:
The system enables self-service by allowing automated generation of workflow process models without requiring specialized developer knowledge for every workflow. Business users can define high-level requirements, and the automated generator handles the complex process modeling, reducing dependency on expensive specialized resources.
Solution Approach 2:
The automated workflow generator serves as an intermediary that bridges the gap between business requirements and technical implementation. It handles the complex process modeling tasks that would otherwise require expensive specialized developers, while still producing models that maintain quality standards.
3Stability of the object's composition
If conventional data management approaches are used, then existing systems are maintained, but efficiency and scalability deteriorate with trillion-scale datasets
Solution Approach 1:
The system replaces mechanical manual processes with automated AI/ML-based workflows. Automated agents perform data governance tasks, process model generation, and workflow execution without human intervention, dramatically improving productivity while maintaining system compatibility through standardized interfaces.
Solution Approach 2:
The system performs preliminary action by automatically generating process models and workflow definitions before data governance operations begin. This pre-computation and pre-modeling approach enables efficient execution of governance tasks on trillion-scale datasets without manual intervention during critical operations.
4Productivity
If automated workflows are implemented, then productivity and autonomy are improved, but system complexity increases
Solution Approach 1:
The automated workflow generator implements universality by using a single AI/ML-based system to perform multiple functions: process model generation, workflow definition, validation, and optimization. This multi-functional approach increases productivity while managing complexity through a unified system rather than multiple separate components.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and systems, based on graph-based data arrangements, among other things, and, more specifically, to a computing platform configured to generate configurable automation templates and automation executable programs that automate workflows based on process models to effectuate automated data governance of datasets, including graph-based data stored in one or more graphs, such as a knowledge graph, a data catalog, etc., whereby in at least one example, automated workflows can provide automatic enrichment of datasets, such as multi-layered knowledge graph data that may include data catalog data. In some examples, a method may include receiving data representing activation of an automated workflow template configured to perform a process workflow, receiving configuration data, linking an automated workflow template to executable instructions, and retrieving data representing a type of process flow, which may data including enriched graph data to modify graph data.