Laboratory Event Contextualization for Workflow Error Isolation
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Solution Overview
Problem
Current laboratory workflows lack a comprehensive data services framework that can analyze event data to model state changes, provide contextualization, and apply contextualized information to enhance workflow performance, particularly in managing complex environments with sensitive samples and equipment.
Innovation Solution
A data services modeling framework that analyzes event data within laboratory workflows, models state changes, and contextualizes event data to explain why such changes occur, publishing messages for executing activities to improve workflow performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive data services framework is implemented to analyze and contextualize event data, then workflow performance and error isolation capability are improved, but system complexity increases
Solution Approach 1:
The patent introduces an event broker as an intermediary component that receives, contextualizes, and routes event data between different system components. This mediator layer handles the complexity of data analysis and contextualization internally, while presenting a simplified interface to external systems. The event broker subscribes to multiple data sources, processes events through contextualization logic, and publishes contextualized events to subscribers, thereby improving workflow reliability without exposing the full system complexity to users.
2Difficulty of detecting and measuring
If detailed event data collection and analysis is performed, then error isolation and cause determination are improved, but data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary contextualization of event data by pre-defining event types, data sources, and contextual relationships before adverse events occur. The system subscribes to and pre-processes event streams, establishing the framework for analysis in advance. When an adverse event occurs, the pre-configured contextualization logic enables rapid error isolation and cause determination without requiring intensive real-time computation, thus reducing data processing time while maintaining high error isolation capability.
Data Source
AI summary
A data services modeling framework provides for event contextualization from state changes in events within laboratory workflows, and for orchestrating activities in workflow cycles. The data services modeling framework forms a part of data services within an orchestrator software package that provides a set of dynamic processes within laboratory workflows, for improving and enhancing workflow performance. The data services modeling framework includes a data services model configured to analyze state changes in event data, by applying algorithms for isolating the activities producing state changes, re-creating an event state, analyzing data points defining state changes to explain the event state at relevant time periods in the workflow cycle. The data services modeling framework also enables publication of messages representing contextualized event data for subscription with the orchestrator environment.