Performance Measurement System Using Common Data Model
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Solution Overview
Problem
Current performance measurement systems in training and simulation environments face challenges in combining and processing real-time data from multiple heterogeneous sources, such as simulator data, neurological, physiological, and observational data, which limits accurate characterization of individual and team performance.
Innovation Solution
A computer-based performance measurement system that uses a common data model format and unique keys to merge and process data from various sources in real-time, enabling multi-modal assessment by translating and parsing performance data into components that can be processed efficiently across multiple processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple heterogeneous sources are combined and processed in real-time, then measurement precision and comprehensiveness of performance characterization are improved, but device complexity and processing difficulty increase
Solution Approach 1:
The system segments the complex task of multi-source data processing into distinct functional components: connectors for data acquisition, payload processors for initial transformation, and model listeners for specific measurements. Each component handles a specific aspect of the data flow, making the overall system more manageable despite handling multiple heterogeneous data sources.
Solution Approach 2:
The common data model serves as an intermediary layer between heterogeneous data sources and the measurement processing system. It provides a standardized format that translates diverse input data (simulator data, neurological data, physiological data) into a unified structure, reducing the complexity of processing multiple data types simultaneously.
2Productivity
If real-time processing of multi-modal data is implemented, then productivity and timeliness of performance feedback are improved, but use of energy and computational resources increase
Solution Approach 1:
The payload processor performs preliminary transformation and validation of incoming data before it reaches the model listeners. By pre-processing data into the common data model format and identifying relevant components upfront, the system reduces the computational burden on subsequent processing stages, enabling real-time performance without excessive resource consumption.
Solution Approach 2:
The system implements selective processing where model listeners only process specific components of the data stream that are relevant to their designated measurements. This partial processing approach allows real-time performance feedback while avoiding the energy cost of processing every data point through every processing stage.
3Ease of operation
If a common data model with unique keys is used to merge data components, then ease of operation and data integration are improved, but device complexity increases due to additional processing layers
Solution Approach 1:
The common data model provides a universal interface that can handle multiple types of heterogeneous data sources through a single standardized format. Unique keys enable the same data structure to represent different data types (simulator events, physiological states, neurological data), making the system easier to operate despite the underlying complexity of integrating diverse sources.
Data Source
AI summary
Methods of and systems to provide performance measurement are provided utilizing an architecture configured to efficiently merge and monitor different types of performance data. Connectors are provided to receive and translate different types of performance data from different sources. The performance data is translated into and stored in a common data model format. In some embodiments, key attributes are defined for each of the performance data sources that uniquely characterizes each relevant performance data so that is can be parsed into separate processing streams to increase system performance. The key attributes also act as cues to organize the performance data as it is being merged so that it can be accessed without requiring a specific source data linkage. Using model listeners, determinations can be quickly made regarding when performance data is changed to reduce calculations necessary to determine measure values. Some embodiments merge different types of performance data in real-time.