Data Store Access Pattern Analyzer for Graphical Models
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Solution Overview
Problem
Existing modeling environments (MEs) face challenges in efficiently analyzing and identifying specified patterns, particularly in data store access, which can lead to inefficiencies and errors due to the lack of effective tools for analyzing data store operations within dynamic systems.
Innovation Solution
The implementation of a graphical modeling environment (ME) that includes a modeling component for constructing and analyzing models, an analyzer component for identifying specified patterns in data store access, and a data store system with writer and reader blocks to track and verify data store operations, enabling static and dynamic analysis of data store access patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing modeling environments are used to analyze data store access patterns, then model analysis can be performed, but the ability to accurately identify specified patterns and detect errors is insufficient
Solution Approach 1:
The patent introduces an intermediary analysis mechanism that acts as a mediator between the modeling environment and data store access operations. This intermediary component systematically tracks and analyzes access patterns, enabling accurate identification of specified patterns and reliable error detection without disrupting the existing modeling workflow.
Solution Approach 2:
The patent implements a feedback mechanism that continuously monitors data store access operations and provides information about identified patterns and potential errors back to the modeling environment. This feedback loop enables real-time pattern recognition and error detection, improving both measurement precision and reliability.
2Measurement precision
If comprehensive analysis of data store operations is implemented, then pattern identification accuracy improves, but the complexity of the modeling environment increases
Solution Approach 1:
The patent segments the comprehensive analysis function into distinct modular components, each responsible for specific aspects of data store access pattern analysis. This segmentation allows the system to perform thorough analysis while maintaining manageable complexity through organized, separable functional units.
Solution Approach 2:
The patent enables the analysis mechanism to automatically perform pattern identification and error detection without requiring extensive manual configuration or intervention. The system self-configures and executes comprehensive analysis autonomously, reducing the perceived complexity for users while maintaining high measurement precision.
3Reliability
If static and dynamic analysis tools are added to the modeling environment, then data store operation verification improves, but the ease of operation decreases
Solution Approach 1:
The patent merges static and dynamic analysis tools into an integrated verification system that operates seamlessly within the existing modeling environment. This combination provides comprehensive data store operation verification while presenting a unified, user-friendly interface that maintains ease of operation.
Solution Approach 2:
The verification system automatically executes static and dynamic analysis without requiring users to manually configure complex parameters or switch between different tools. The system performs verification autonomously, improving reliability while preserving ease of operation through automated self-service functionality.
Data Source
AI summary
In an embodiment, a technique for analyzing a model, either statically or dynamically, to check the model for one or more specified patterns with respect to accessing a data store associated with the model. The patterns may include, for example, writing to a data store prior to reading the data store, reading the data store prior to writing the data store, writing to the data store multiple times prior to reading the data store, reading the data store multiple times before writing the data store, etc. The model may be an executable graphical model that is generated in a graphical modeling environment. A result may be generated based on the analyzing. The result may be output.