Data Conversion Platform Pattern Matching
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Solution Overview
Problem
Large organizations face challenges in efficiently managing their complex computer systems, particularly in exchanging information between internal and external systems, due to differences in data formats and types, leading to misinterpretation and incorrect data import.
Innovation Solution
A data conversion platform that automatically parses and converts data from various formats into a common format usable by internal computing systems, utilizing pattern models and match files to ensure accurate data transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data conversion methods are used to handle diverse external data formats, then data processing accuracy can be maintained, but system complexity and processing time increase significantly
Solution Approach 1:
The patent introduces an intermediary data conversion system that sits between external data sources and internal applications. This intermediary automatically detects data formats, applies appropriate conversion rules, and transforms data into standardized internal formats, thereby maintaining accuracy while reducing the complexity burden on individual applications.
Solution Approach 2:
The conversion system is designed with universal capabilities to handle multiple data formats (XML, JSON, CSV, fixed-width, etc.) through a single platform. It provides format-agnostic interfaces and can be integrated into various applications, eliminating the need for separate conversion mechanisms for each application and reducing overall system complexity.
2Adaptability or versatility
If multiple custom conversion programs are developed for different data formats, then specific data transformation requirements can be met, but development time and maintenance effort increase
Solution Approach 1:
The system provides a universal conversion platform that supports multiple data formats through a single integrated solution. It includes pre-built connectors for common formats and a rule-based engine that can adapt to new formats without requiring complete program rewriting, thus reducing development time while maintaining versatility.
Solution Approach 2:
The conversion system employs dynamic rule-based transformation logic that can be configured and adjusted without recompiling code. Conversion rules are stored in configurable formats and can be modified to accommodate new data sources or changing requirements, enabling rapid adaptation with minimal development effort.
3Productivity
If automated data import is implemented without format validation, then processing speed increases, but data interpretation errors occur
Solution Approach 1:
The system performs preliminary validation and format detection before executing the main conversion process. It checks data structure, validates required fields, and verifies format compliance upfront, allowing fast processing of validated data while catching errors early, thus maintaining both speed and reliability.
Solution Approach 2:
The conversion system incorporates feedback mechanisms that monitor conversion success rates and detect patterns of errors. When interpretation errors are detected, the system can adjust its conversion rules, request clarification, or flag problematic records for review, thereby improving reliability while maintaining automated high-speed processing.
Data Source
AI summary
Various aspects of the disclosure relate to automated conversion or transformation of data received from a plurality of sources, and of any format, into forms compatible as input to an application computing system. The data conversion platform identifies a format of an input data file or structure and imports characters, both printable and non-printable, from the input file and stores the imported characters as a string. The data conversion platform generates from the string data to an array of strings based on delimiters or other special characters. The data conversion platform applies a pattern model to the array string to identify patterns of data and stores matches as a second array of strings. The second array of strings is compared to a match file to identify bindings between input data and desired output data. These matches are then formatted as output data and communicated to the desired application for processing.