Data Parsing Module Schema Generation for Opaque Records
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Solution Overview
Problem
Data processing systems face challenges in accurately parsing opaque data records due to errors in data dictionaries, leading to inefficiencies and inaccuracies in data processing, particularly when dealing with opaque data formats like fixed field records without self-describing structures.
Innovation Solution
The implementation of a data parsing module that generates a schema from a provided data dictionary, using tools like Tabula for extracting tables from PDF documents, and an automatic parser generator to create computer-readable program instructions for parsing opaque data records, ensuring accurate identification and extraction of data fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data processing systems use traditional parsing methods for opaque data records, then the parsing process is simple, but accuracy deteriorates due to errors in data dictionaries
Solution Approach 1:
The patent introduces an intermediary schema generation process that acts as a mediator between the data dictionary and the parsing logic. The schema generation module translates the data dictionary into a structured schema representation, which then guides the parsing process. This intermediary layer isolates the parsing logic from direct dependency on the data dictionary, allowing for more accurate and flexible parsing while maintaining system manageability.
Solution Approach 2:
The patent applies preliminary action by generating the parsing schema in advance before the actual data parsing occurs. The schema generation module pre-processes the data dictionary to create a structured schema that defines the expected data structure. This preliminary schema generation enables the parsing process to proceed with higher accuracy by having predefined rules and structures to follow, rather than attempting to parse opaque data without prior preparation.
2Productivity
If data processing systems manually process opaque data records, then flexibility is maintained, but productivity deteriorates due to manual intervention requirements
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate parsing schemas from data dictionaries without requiring manual intervention. The schema generation module autonomously processes the data dictionary input and produces the necessary parsing configurations. This automation significantly improves productivity by eliminating manual schema creation steps while maintaining the flexibility needed for handling various opaque data formats.
Solution Approach 2:
The patent replaces manual mechanical operations with an automated schema generation mechanism. Instead of manually analyzing and creating parsing rules for opaque data records, the system uses an automated module that transforms data dictionary information into executable parsing schemas. This substitution of manual mechanical work with an automated intelligence-based system dramatically increases data processing throughput while preserving operational flexibility.
3Reliability
If data dictionaries contain errors, then data processing can proceed quickly, but reliability deteriorates due to incorrect data extraction
Solution Approach 1:
The patent addresses reliability concerns by performing preliminary validation and processing of the data dictionary during schema generation. Before actual data parsing occurs, the system pre-processes the data dictionary to create a structured schema that can be validated for consistency. This preliminary action allows the system to detect and handle potential errors in the data dictionary before they affect data extraction, thereby improving reliability without significantly impacting processing time.
Solution Approach 2:
The schema generation module serves as an intermediary that buffers the impact of data dictionary errors on the parsing process. By translating the data dictionary into a structured schema format, the system creates an intermediate representation that can be more rigorously validated and controlled. This intermediary layer allows for better error detection and handling, improving data extraction reliability while minimizing the time penalty through efficient automated processing.
Data Source
AI summary
Systems and methods for converting a data item provided by an external data provider system into a data type specified by a data processing system for a data field of the data item. A data processing system stores a coercion rule for each data field of a first data set provided by the data provider system. Each stored coercion rule identifies at least one data type for the corresponding data field. Responsive to a second data set provided by the data provider system, the data processing system coerces each data item of the second data set into at least one data type specified by the stored coercion rule for the data field of the data item to generate at least one converted data item of the second data set. The data processing system generates information from at least one converted data item, and provides the information to a consuming system.