Automated Metadata Lookup for Legacy System Data Extraction
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Solution Overview
Problem
The integration and consolidation of heterogeneous enterprise computing systems, particularly after mergers or acquisitions, is challenging due to differences in data storage structures and models, leading to complex and error-prone manual processes for data extraction and access.
Innovation Solution
A method and system for automating metadata lookup and data extraction from legacy systems by identifying and prioritizing views associated with queries, allowing software applications to access legacy data efficiently without manual table joining, using a process that translates queries into commands understandable by the legacy systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used for data extraction and access from legacy systems, then flexibility and adaptability to different data storage structures are maintained, but the process becomes complex and error-prone
Solution Approach 1:
The patent introduces an intermediary layer consisting of metadata tables and views that mediate between the application layer and the legacy system's physical data storage. This intermediary automatically translates application queries into legacy system-specific queries, eliminating the need for manual table joining processes while reducing errors and simplifying the overall data extraction workflow.
Solution Approach 2:
The patent performs preliminary actions by pre-defining metadata tables and views that capture the structure and relationships of legacy system data before actual data extraction occurs. These pre-configured metadata structures enable automated query translation and eliminate the need for complex manual data access processes during runtime.
2Ease of manufacture
If consolidation of enterprise computing systems is pursued, then licensing, maintenance, and personnel costs are reduced, but integration challenges arise due to different data storage structures and models
Solution Approach 1:
The patent creates a universal metadata layer that can interface with multiple different legacy system data storage structures and models. This universal metadata framework enables a single consolidated system to access and integrate data from diverse legacy systems with different vendors and generations, overcoming integration challenges while achieving cost reductions through consolidation.
Solution Approach 2:
The metadata tables and views serve as intermediaries that translate between different legacy system data models and the consolidated system's data access requirements. This intermediary layer absorbs the integration complexity, allowing the consolidated system to benefit from reduced costs without being burdened by the heterogeneity of underlying legacy systems.
3Productivity
If automated metadata lookup is implemented, then data extraction efficiency is improved and errors are reduced, but system complexity increases due to additional metadata management
Solution Approach 1:
The patent implements self-service through automated metadata lookup mechanisms that automatically resolve data access requirements without manual intervention. The system autonomously translates queries, joins tables, and extracts data based on pre-configured metadata, improving productivity while containing complexity through automation rather than manual processes.
Solution Approach 2:
The patent replaces manual mechanical processes of data extraction and table joining with automated electronic metadata lookup and query translation systems. This substitution improves productivity by eliminating repetitive manual tasks while managing complexity through software-based automation rather than human operations.
Data Source
AI summary
A method and system for accessing data in a de-commissioned legacy system are provided. Data are automatically extracted from the legacy system, although data structure(s) of the legacy system might not be known, by finding views corresponding to a query for the data. Attributes, metadata, and/or fields (“attributes”) can be parsed from the query. Tables and/or fields including the parsed attributes are identified. Views can be then identified, where the views contain the tables and/or fields including the parsed attributes. The views can be ranked in an order from those that include the greatest number of parsed attributes to those including the least number of parsed attributes. A data request understandable by the legacy system, e.g. a packet, can then be formed using the least number of views, where the views can collectively include all of the parsed attributes.