Time-Series Assessment Evaluation With Traceable Expression Trees
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Solution Overview
Problem
Existing evaluation systems for assessing time-series data in simulation models and physical systems face challenges in real-time evaluation, traceability, and resource management, leading to difficulties in debugging and determining the cause of assessment failures.
Innovation Solution
The system employs an assessment editor, expression builder, and evaluator that create and evaluate formal mathematical expressions as expression trees, providing traceability and real-time monitoring, while managing resources through buffer size determination to prevent memory and processor overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time evaluation of assessments is implemented, then evaluation speed and responsiveness are improved, but system complexity and resource consumption increase
Solution Approach 1:
The system divides the assessment evaluation process into distinct modular components: an assessment editor for creating assessments, an expression builder for constructing evaluation expressions, and an evaluator for executing assessments. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while enabling real-time evaluation capabilities.
Solution Approach 2:
The patent introduces intermediate data structures and buffering mechanisms that mediate between data sources and the evaluation engine. These intermediaries manage data flow and resource allocation efficiently, allowing real-time evaluation without overwhelming the system with raw data processing complexity.
2Difficulty of detecting and measuring
If comprehensive traceability is provided for assessment failures, then debugging capability is improved, but data processing overhead increases
Solution Approach 1:
The system pre-computes and stores intermediate results and metadata during the assessment evaluation process, before failures occur. This preliminary action includes capturing expression trees, data source information, and evaluation states, which are then readily available for debugging without requiring re-computation, thus reducing processing overhead while maintaining comprehensive traceability.
Solution Approach 2:
The patent creates and maintains copies of assessment expressions, data structures, and evaluation states throughout the process. These copies serve as traceability records that can be examined for debugging without affecting the primary evaluation flow, enabling comprehensive failure analysis with minimal impact on processing efficiency.
3Reliability
If buffer sizes are increased to prevent resource overload, then system stability is improved, but memory consumption increases
Solution Approach 1:
The system implements dynamic buffer size management where buffer allocations are adjusted based on actual evaluation needs and system conditions. Buffers are allocated and deallocated dynamically during assessment execution, allowing the system to maintain stability during peak loads while minimizing memory consumption during idle periods. This dynamic approach replaces static large buffer allocations with adaptive memory management.
Solution Approach 2:
The patent employs parameter changes in buffer management by adjusting buffer sizes, allocation strategies, and cleanup timing based on assessment characteristics and system state. Different assessment types and data sources trigger different buffer parameter configurations, optimizing the balance between stability and memory usage for each specific evaluation scenario.
Data Source
AI summary
Systems and methods evaluate assessments on time-series data. An expression including temporal operators may be created for an assessment. The expression may be arranged in the form of an expression tree having nodes representing input data to the assessment and intermediate results of the expression. An assessment may be evaluated by performing a bottom-up traversal of the expression tree. One or more plots may be generated including a plot of the outcome of the assessment, e.g., pass, fail, or untested, plots of intermediate results of the expression and plots of input data as a function of time. Graphical affordance may be presented on the plots that mark the regions that may have contributed to a specific pass or fail result of the assessment, and points within the regions that resulted in the assessment passing or failing.