Data Store Architecture Detection for Evolving Schema Tracking
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Solution Overview
Problem
Existing systems fail to accurately reflect the actual data store architecture due to discrepancies between the initial design document and the evolving data store, leading to incorrect operations and inaccurate results, especially when applications are implemented over time, and require human effort to determine an approximation of the data store architecture.
Innovation Solution
A complementary system comprising a 'Data Store Analyzer' module with components for architecture analysis, statistics compilation, and composition, along with a 'Queries Analyzer' module for query analysis, to automatically recognize and track data store architecture changes and evolution, generating a precise approximation without relying on user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the initial design document is used to represent data store architecture, then the architecture information is initially accurate, but it becomes inaccurate over time as applications are implemented and modify the data store
Solution Approach 1:
The system enables self-service by automatically detecting and analyzing data store architecture changes without requiring manual intervention. The analyzer autonomously queries the data store, identifies structural modifications, and updates the architecture representation, allowing the system to maintain accuracy through self-updating mechanisms rather than relying on external documentation updates.
Solution Approach 2:
The system implements feedback by continuously monitoring the data store for changes and comparing the actual architecture against the represented architecture. When discrepancies are detected, the system generates feedback loops that trigger automatic updates to the architecture representation, ensuring the documentation remains synchronized with the actual data store structure through continuous verification and correction.
2Loss of information
If manual tracking of data store architecture is performed, then human understanding of architecture is maintained, but significant human effort is required to determine architecture approximation
Solution Approach 1:
The system replaces manual mechanical analysis with automated computational analysis. Instead of human experts manually examining data store structures and documenting changes, the system employs automated analyzers that query the data store, detect structural modifications, and generate architecture representations programmatically, substituting human cognitive effort with automated information processing systems.
Solution Approach 2:
The system introduces an intermediary analyzer component that acts as a mediator between the data store and human users. This intermediary automatically captures architecture information, processes changes, and presents synthesized architecture representations to users, eliminating the need for users to directly engage in complex manual tracking while preserving comprehensive architecture understanding.
3Adaptability or versatility
If standard data management tools are used, then simple company requirements are satisfied, but sophisticated data store management capabilities are insufficient for complicated needs
Solution Approach 1:
The system implements dynamics by providing adaptive architecture analysis that adjusts to varying complexity requirements. The analyzer can operate at different levels of sophistication depending on the data store's complexity, automatically detecting changes in relational structures, hierarchical models, or other data store types, and scaling its analysis capabilities to match the specific needs of simple or complex data management scenarios.
Data Source
AI summary
A system is configured for automatic recognition of data store architecture and tracking dynamic changes and evolution in data store architecture. The system is a complementary system, which can be added onto an existing data store system using the existing interfaces or can be integrated with a data store system. The system comprises three main components that are configured to compose an approximation of the data store architecture. The first of these components is adapted to execute an analysis of the architecture of the data store; the second of the components is adapted to collect and compile statistics from said data store; and the third of the components is adapted to compose an approximation of the architecture of said data store.