Generic Data Extraction Framework Using Modular Plug-ins
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Solution Overview
Problem
Conventional data extraction techniques face challenges in portability across various platforms, requiring significant time, money, and resources for code modification, rework, testing, and maintenance when transitioning between software projects, due to their focus on specific data formats and interfaces.
Innovation Solution
The GeDEAR framework separates data extraction, reduction, and analysis functions from specific hardware, software, and interface protocols, allowing for customizable plug-ins and execution on multiple hardware and software systems, enabling reuse across projects without additional dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional data extraction techniques are used with specific data formats and interfaces, then the extraction process is simple and focused, but portability to various platforms is poor and requires significant code modification
Solution Approach 1:
The system segments data extraction functionality into independent plug-in modules that can be selectively loaded and configured. Each plug-in handles specific data formats or interfaces, allowing the core extraction engine to remain platform-agnostic while adapting to different requirements through modular components.
Solution Approach 2:
The extraction engine is designed as a universal platform that can handle multiple data formats and interfaces through standardized plug-ins. The system provides multi-functionality by enabling the same core engine to work across different platforms and projects without requiring fundamental code changes, achieving both simplicity and adaptability.
2Manufacturing precision
If data extraction code is customized for specific projects, then the extraction meets project-specific requirements, but time and resources for code modification and maintenance increase
Solution Approach 1:
The system performs preliminary configuration through plug-in registration and parameter setup before actual data extraction begins. Templates and configuration files allow extraction rules, data formats, and interface specifications to be predefined and reused across projects, eliminating the need for extensive code customization while maintaining extraction accuracy.
Solution Approach 2:
The system enables adaptation to different projects by changing configuration parameters rather than modifying code structure. Plug-ins expose configurable parameters that can be adjusted to meet project-specific requirements, allowing the same extraction engine to handle diverse data formats and interfaces through parameter tuning instead of code rewriting.
Data Source
AI summary
A system and a method are provided for processing data from a computer that executes an application program. The system includes an extraction engine, a reduction program and an analysis program. The extraction engine retrieves execution data from the application program and records the data to nonvolatile memory as extract data. The reduction program reads the extract data from the memory and reduces the data into human readable format as reduce data. The analysis program analyzes the reduce data in accordance with operator-provided instructions and produces event data that identify at least one sequence associated with the execution data. A management console further displays the implemented processes. The extraction engine can further include a classes assigner for categorizing said execution data into classes. The reduction program can further include a structure builder for formatting said extract data. The reduction program can further include an event layout builder for formatting said event data.