Content Transformation Engine for Legacy Data Integration
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Solution Overview
Problem
Legacy systems store data in formats like relational databases that are not readily accessible or usable in web-based applications, making it difficult for businesses to monitor and integrate their data effectively due to lack of web-accessible formats such as RSS, XML, or HTML.
Innovation Solution
The system employs FEEDLETS and FUSELETS to transform and correlate raw data from legacy systems into web-accessible formats like RSS, HTML, or XML, allowing for the generation of complex composite data feeds that can be easily integrated into web-based applications, enabling current-status monitoring and human actionable content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in legacy relational database formats, then data storage capacity and complexity are improved, but data accessibility and usability in web-based applications deteriorate
Solution Approach 1:
The patent introduces an intermediary system that includes a legacy system interface, data extraction module, and transformation engine. This intermediary layer sits between the legacy relational database and web-based applications, extracting data from the legacy system, transforming it into web-accessible formats (XML, RSS, HTML), and presenting it to web applications. This resolves the contradiction by maintaining the legacy storage system while adding an intermediate layer that provides web-friendly access.
Solution Approach 2:
The patent segments the data access architecture into distinct functional modules: a legacy system interface layer, data extraction layer, transformation layer, and web presentation layer. Each layer handles specific tasks independently, allowing the legacy database to maintain its complex storage structure while the transformation layer converts data into simplified web-accessible formats. This segmentation enables both high-capacity storage and easy web access without compromising either aspect.
2Adaptability or versatility
If data is extracted and transformed into multiple web-accessible formats, then data usability and integration capability are improved, but system complexity and processing requirements increase
Solution Approach 1:
The patent implements a universal transformation engine that can output multiple web-accessible formats (XML, RSS, HTML) from a single data extraction process. The transformation module is designed to handle multiple format types through a unified architecture, where the same extracted data can be transformed into different formats as needed. This multi-functional approach increases adaptability and integration capability while avoiding the need for separate extraction systems for each format, thereby controlling system complexity.
3Loss of information
If complex composite data feeds are generated from multiple sources, then data comprehensiveness and analytical value are improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary data extraction and transformation operations that prepare data from multiple legacy sources in advance. The system extracts data from multiple sources, transforms it into standardized formats, and makes it readily available for composite feed generation. This preliminary preparation reduces the computational burden and processing time when complex composite data feeds need to be generated, as the raw data is already extracted and transformed and ready for integration.
Data Source
AI summary
A method for communicating data includes interfacing different data sources to stand-alone software agents customized for the different data sources. The method also providing selected source data to the stand-alone software agents to generate first-stage data feeds in a neutral format in accordance with the customization. The selected source data is dynamically selected from within the data sources, and transformed into the first-stage data feeds in the neutral format. The stand-alone software agents send the first-stage data feeds to an aggregation agent. The aggregation agent generates for a user and based on specified criteria, a second-stage output as a composite of selected source data from the first-stage data feeds.