Graph Database Schema Evolution for Unstructured Data
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Solution Overview
Problem
Existing relational database systems are inadequate for managing unstructured data of unknown structure or type and uncertain relationships, as they impose rigid constraints that cannot adapt to changing data and relationships, making it difficult to capture and analyze relevant information effectively.
Innovation Solution
A system and method that uses a graph-based schema to store and manage unstructured data, allowing for the generation of nodes and edges to represent relationships, with an inference engine that infers structure and captures uncertainty, enabling the system to evolve based on user input and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a relational database model is used to organize data into tables with fixed schemas, then data storage and retrieval are efficient and structured, but the system cannot adapt to unstructured data of unknown structure or type and cannot capture uncertain relationships between data pieces
Solution Approach 1:
The patent implements a dynamic schema evolution mechanism where the database schema automatically adapts to incoming unstructured data. The system uses schema evolution capabilities to transform static relational schemas into flexible structures that can accommodate unknown data types and relationships, allowing the database to evolve its structure based on actual data characteristics rather than requiring predetermined schemas.
Solution Approach 2:
The system changes the fundamental parameters of data organization by transitioning from fixed relational tables to a more flexible model that can represent unstructured data. This involves changing how data is stored, indexed, and queried, allowing the system to handle variable data structures, unknown types, and uncertain relationships while maintaining operational efficiency.
2Adaptability or versatility
If a fixed relational schema is imposed on data collection, then data organization is consistent and queryable, but the system cannot accommodate changing business processes or evolving data relationships
Solution Approach 1:
The patent implements preliminary schema evolution mechanisms that anticipate and prepare for changing data relationships. The system proactively evolves the schema based on emerging data patterns and relationship changes, rather than waiting for explicit schema modification requests. This allows the database to adapt to evolving business processes automatically, reducing the time loss associated with manual schema changes.
Solution Approach 2:
The system incorporates feedback loops that monitor data relationships and automatically trigger schema evolutions when changes are detected. This feedback mechanism allows the database to respond dynamically to changing business processes and data relationships, maintaining consistency and queryability without requiring manual intervention or time-consuming schema modifications.
3Loss of information
If comprehensive data attributes are captured upfront in a relational model, then future queries can be answered efficiently, but it is difficult for people to capture and input all relevant attributes in a consistent manner
Solution Approach 1:
The patent implements self-service data capture mechanisms where the database system automatically infers and captures data attributes without requiring manual specification. The system uses intelligent algorithms to identify relevant attributes, data types, and relationships from unstructured input, eliminating the need for users to predefine comprehensive schemas. This self-service approach maintains data completeness while significantly improving ease of operation.
Solution Approach 2:
The system replaces manual mechanical processes of data attribute specification with automated intelligent systems. Instead of requiring users to manually define and input all possible attributes in a consistent manner, the patent employs automated inference engines and machine learning algorithms that automatically capture and organize data attributes, reducing human effort while maintaining or improving data completeness.
Data Source
AI summary
A technique for running queries is provided that includes a method and system for managing unstructured data and for capturing uncertain relationships between pieces of data. A structural schema is generated from unstructured data that is configured to evolve in response to user input and incoming data that is changing over the course of an application. The schema is also configured to capture relationships between data that are uncertain or difficult for a person to capture in a consistent or comprehensive manner. The technique is especially advantageous for running and returning meaningful responses to queries that require an ability to connect pieces of data received from unstructured data or disparate sources, including user input, or where the query is directed to information that is uncertain or was not anticipated as useful or relevant at the time the data containing the information was originally received.