Configurable Data Conversion System for Flexible Output Formatting
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Solution Overview
Problem
Existing data conversion techniques for databases are often rigid and resource-intensive, requiring significant processing and memory resources, and are not adaptable to varying output formats and content needs.
Innovation Solution
A system that converts database data into markup language (XML) and organizes it into logical groups and sub-groups based on user-configurable parameters, allowing for efficient transformation and output formatting using markup transformation languages like XSLT, with the ability to split data into smaller sets to reduce processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If data is converted using rigid conversion techniques designed for specific output, then conversion accuracy for that specific output is improved, but adaptability to varying output formats deteriorates
Solution Approach 1:
The patent implements a universal data conversion system that can handle multiple output formats (XML, CSV, JSON, etc.) through a single configurable framework. The conversion module is designed to work with any database schema and produce various output formats by changing configuration parameters rather than requiring separate conversion tools for each format, thus achieving both precision and adaptability.
Solution Approach 2:
The system uses parameter-based configuration to adapt to different output requirements. By modifying conversion parameters, output format specifications, and schema mappings without changing the underlying conversion engine, the system can accurately convert to different formats while maintaining conversion precision for each specific output type.
2Productivity
If traditional data conversion techniques are used, then data conversion capability is provided, but processing resources and memory usage increase
Solution Approach 1:
The patent divides the data conversion process into distinct modular components: schema analysis module, conversion rule engine, output generation module, and validation module. Each module handles a specific aspect of conversion, allowing for optimized resource allocation and parallel processing where applicable, thereby reducing overall processing resource requirements while maintaining conversion capability.
Solution Approach 2:
The system performs preliminary schema analysis and conversion rule generation before actual data conversion. By pre-processing the schema to generate conversion templates and rules, the system avoids redundant processing during actual data conversion, significantly reducing processing resources and memory usage during the main conversion operation.
3Productivity
If data is organized into logical groups and split into smaller sets, then processing efficiency is improved, but data organization complexity increases
Solution Approach 1:
The patent automatically segments retrieved data into logical groups based on schema relationships and conversion requirements, then divides these groups into smaller processable sets. This segmentation is performed algorithmically based on data characteristics rather than manual organization, improving processing efficiency while keeping the organization logic transparent and automated rather than complex.
Data Source
AI summary
An apparatus is provided that includes a processor and memory storing executable instructions that in response to execution by the processor cause the apparatus to at least perform a number of operations. The apparatus is caused to receive a rowset of data from a data source, where the rowset includes one or more rows of values from the data source. The apparatus is caused to convert the rowset to markup data, and organize the markup data in preparation for its transformation to an output according to a markup transformation language. The markup data includes, for each row of the rowset, a row markup element; and the organized markup data includes an organization of the row elements into a plurality of logical groups. Even further, the organized markup data may be split into a plurality of smaller markup data sets, which may facilitate transformation of the markup data.