Dynamic Data Structure Adaptation for Hazy External Data
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Solution Overview
Problem
Existing database systems face challenges in storing and managing data from external sources with unknown data contexts and structures, as flat files are difficult to query and analyze, and each source may have different formats and contexts, requiring dynamic data structure adaptation for efficient storage and retrieval.
Innovation Solution
A method that extracts key-value pairs from inbound data blocks, calculates similarities with existing data structures, creates new structures if necessary, and integrates data into existing structures based on similarity thresholds, using Data Definition Language (DDL) and Data Manipulation Language (DML) commands to manage and consolidate data structures dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data from external sources with unknown structures is stored as flat files, then storage simplicity is improved, but queryability and analysis capability deteriorate
Solution Approach 1:
The system dynamically creates and adapts data structures based on incoming data characteristics. When data with unknown structure arrives, the system automatically generates appropriate schemas, indexes, and storage formats, transforming static storage into a dynamic adaptation process that balances simplicity with queryability.
Solution Approach 2:
The system performs self-service by automatically analyzing incoming data, determining its structure and context, and creating appropriate storage schemas without manual intervention. This self-configuration capability allows the system to maintain storage simplicity while ensuring data is properly structured for querying and analysis.
2Loss of information
If separate data structures are created for each external data source, then data context preservation is improved, but system complexity deteriorates
Solution Approach 1:
The system implements a universal data structure framework that can accommodate multiple data sources with different contexts. Rather than creating entirely separate structures for each source, the system uses a unified approach with configurable schemas that preserve source-specific context through metadata and contextual attributes within the universal structure.
Solution Approach 2:
The system segments data context preservation into hierarchical levels: a universal top-level structure for common operations, with source-specific segments for preserving unique contexts. This segmentation allows the system to maintain overall simplicity while preserving detailed context information where needed.
3Manufacturing precision
If data structures are manually created for each incoming data format, then data structure accuracy is improved, but processing time deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-defining template structures and schemas for common data types and formats. When new data arrives, the system matches it against these pre-prepared templates, significantly reducing processing time compared to creating structures from scratch, while maintaining accuracy through the predefined structural guidelines.
Solution Approach 2:
The system implements feedback mechanisms where initially manually created accurate structures are analyzed and used to refine automated structure generation algorithms. Over time, the system learns from manual corrections and feedback, improving the accuracy of automatic structure creation while maintaining fast processing speeds.
Data Source
AI summary
A method for storing data of an unknown data context and structure in a structured storage system is described. The method includes extracting a list of key-value pairs from one or more inbound data blocks from one or more external data sources; calculating similarities for the list of key-value pairs from the one or more inbound data blocks based on one or more current existing data structures; determining a highest similarity from the similarities for the list among the one or more current existing data structures; determining that the highest similarity for the list does not meet a predetermined similarity threshold; creating a new data structure to store the list of key-value pairs for a first inbound data block from the one or more inbound data blocks; and storing the list of key-value pairs in the new data structure in the structured storage system.