Context-Based Workflow Processing Using Knowledge Graphs
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Solution Overview
Problem
Existing digital workflow systems require tedious and error-prone user interaction for gathering contextual information, leading to inefficient and inaccurate query resolution.
Innovation Solution
Implement a context-based digital workflow system that utilizes a knowledge graph to aggregate and analyze data from multiple sources, providing augmented contextual information to streamline query resolution and reduce user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a traditional digital workflow system is used, then the system can process queries, but it requires tedious and error-prone user interaction for gathering contextual information
Solution Approach 1:
The system performs preliminary actions by proactively gathering contextual information from multiple data sources (HR systems, IT ticketing systems, device management systems) before the user submits their query. This pre-gathering of data eliminates the need for users to manually provide contextual information, thereby reducing interaction burden while improving accuracy through systematic data collection from authoritative sources.
Solution Approach 2:
The system introduces an intermediary component that acts as a mediator between the user and multiple data sources. This intermediary automatically retrieves and synthesizes contextual information from various systems (employee profiles, device inventories, incident histories) without requiring direct user interaction with each source, thereby simplifying the user experience while ensuring comprehensive and accurate data gathering.
2Productivity
If users manually provide contextual information, then the workflow can be executed, but the process becomes time-consuming and redundant
Solution Approach 1:
The system implements self-service by automatically retrieving all necessary contextual information from connected data sources without requiring user input. The workflow engine autonomously queries HR systems for employee details, IT systems for device information, and incident databases for historical context, thereby eliminating redundant manual data gathering and significantly reducing the time required to prepare for query resolution.
Solution Approach 2:
The system merges multiple data retrieval operations into a single unified workflow execution. Instead of requiring users to manually gather information from multiple sources sequentially, the system combines automated queries across HR, IT, and device management systems into one coordinated process, thereby reducing data gathering time while maintaining comprehensive information collection.
3Measurement precision
If contextual information is gathered manually, then user-specific details can be obtained, but errors are frequently introduced
Solution Approach 1:
The intermediary component ensures measurement precision by systematically retrieving contextual data from authoritative sources rather than relying on manual user input. The intermediary queries established data systems (HR databases, IT asset management systems) that maintain accurate, validated information, thereby eliminating transcription errors and ensuring high precision in contextual data while improving overall reliability through automated, consistent data collection processes.
4Measurement precision
If a knowledge graph is implemented to store contextual data, then query accuracy improves, but system complexity increases
Solution Approach 1:
The knowledge graph implements preliminary action by pre-processing and organizing contextual information from multiple data sources into structured relationships before queries are submitted. Entities such as employees, devices, and incidents are pre-linked with their attributes and relationships, enabling the system to rapidly retrieve accurate contextual data during query resolution. This pre-organization improves query accuracy while managing complexity through upfront data structuring rather than complex real-time processing.
Data Source
AI summary
Data from a plurality of data sources is received. A knowledge graph is generated using the received data to discover user relationships between elements of the data from the plurality of data sources. A query associated with a workflow is received from a user. Using an application programming interface, the knowledge graph is queried for data associated with the user and the query. A recommendation associated with the workflow is determined by analyzing a result of the query of the knowledge graph.