Data Comparator System Using Configurable Metadata
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Solution Overview
Problem
Traditional methods for comparing data across different data environments are cumbersome, requiring re-entry, reformatting, and custom scripting, leading to inefficiencies and increased development time, while also being resource-intensive and prone to errors.
Innovation Solution
A system that utilizes configurable metadata and a generic script set to enable data comparison across platforms, reducing data duplication, processing overhead, and user interactions, and enhancing data reliability and accuracy by allowing direct comparison of real-time data through a data comparator system with modules for data extraction, comparison, and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used for data comparison across different data environments, then data can be compared, but the process requires re-entry, reformatting, and custom scripting leading to increased development time and resource consumption
Solution Approach 1:
The patent implements a universal data comparison platform that can handle multiple data sources, formats, and comparison scenarios through a single standardized interface. The system uses configurable metadata schemas and template-based comparison logic that can be adapted to different data environments without requiring custom scripting for each case, thereby eliminating reformatting and re-entry requirements while reducing development time.
Solution Approach 2:
The system changes the parameters of data comparison by introducing configurable metadata that defines data structures, relationships, and comparison criteria. Instead of hardcoding comparison logic for each data environment, the system uses parameterized metadata schemas that can be configured to match different data sources, allowing the same core comparison engine to handle diverse scenarios efficiently.
2Adaptability or versatility
If traditional methods are used for data comparison, then data analysis can be performed, but the systems are hard coded and system specific making comparison between differing data environments difficult and time consuming
Solution Approach 1:
The patent creates a universal data comparison platform that can operate across multiple data environments and systems. By implementing standardized metadata schemas and template-based comparison logic, the system achieves cross-platform compatibility without requiring separate customized systems for each data environment, thereby increasing adaptability while managing complexity through standardization.
Solution Approach 2:
The system introduces metadata schemas and comparison templates as intermediary layers between different data sources and the comparison engine. These intermediaries translate diverse data formats and structures into a unified representation that the comparison logic can process, enabling cross-platform comparison without direct complexity between systems.
3Adaptability or versatility
If data is stored in numerous different data storage formats in various locations, then diverse application parameters can be serviced, but data extraction and comparison becomes resource-intensive and processing overhead increases
Solution Approach 1:
The patent applies preliminary action by pre-defining metadata schemas that describe data structures, relationships, and extraction rules before actual data processing occurs. These pre-configured schemas enable the system to efficiently extract and compare data from various formats without requiring intensive real-time processing for format conversion, thereby reducing processing overhead while maintaining format compatibility.
Solution Approach 2:
The system uses parameterized metadata that can be configured to match different data storage formats and locations. By changing the parameters in the metadata schemas rather than the underlying processing logic, the system can handle diverse data formats efficiently without requiring resource-intensive format conversion or custom processing for each source.
4Reliability
If complex data transformations are performed to derive valuable insights, then data analysis capability is enhanced, but the transformations are system specific making them difficult to replicate across different systems
Solution Approach 1:
The patent implements universal comparison logic that operates across different systems through standardized metadata schemas. The comparison engine uses parameterized templates that can be configured to replicate data transformations across different environments without being system-specific, thereby ensuring both reliability and portability of analysis results.
Solution Approach 2:
The system copies comparison logic and metadata schemas between different systems rather than adapting system-specific transformations. By replicating the configured metadata and comparison templates across environments, the system ensures consistent and reliable data analysis that can be easily ported and replicated without system-specific dependencies.
Data Source
AI summary
A system for data comparison is disclosed. The system may receive a source configuration metadata. The system may configure a data extraction module to extract data from a data set in response to the source configuration metadata. The system may generate a pre-work data from the data set. The system may compare the pre-work data to generate a post-process data set. The system may generate a report corresponding to the post-process data set.