Semantic Data Graph Layer for Data Warehouse Schema Evolution
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Solution Overview
Problem
Existing data warehousing technologies face challenges in complex data relationship mapping, lack of semantic understanding, manual schema evolution management, inflexible audience building, and barriers for non-technical users interacting with data warehouses.
Innovation Solution
A data graph specification using a configuration language defines data entities and relationships, incorporating semantic meaning interpretation, automated schema evolution tracking, and a user-friendly interface with a public API to facilitate dynamic audience building and reduce technical barriers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data warehousing methods are used to store and organize data, then data can be efficiently stored and accessed, but the system lacks semantic understanding and cannot accurately interpret complex data relationships
Solution Approach 1:
The patent introduces an intermediary layer (semantic layer or data model layer) between the physical data warehouse and the user/query interface. This intermediary layer captures semantic meanings, relationships, and business logic, allowing the system to understand and interpret data relationships without adding complexity to the underlying storage structure. The intermediary translates complex queries into efficient data warehouse operations while preserving semantic context.
2Reliability
If manual methods are used to manage schema evolution, then changes can be tracked, but human error increases and efficiency decreases
Solution Approach 1:
The patent implements automated schema evolution management where the system itself monitors, detects, and applies schema changes without requiring manual intervention. The system automatically tracks changes in data structures, validates them against the data model, and updates the schema accordingly, eliminating human error while accelerating the evolution process. This self-service approach maintains reliability through automated validation while improving productivity through continuous operation.
3Measurement precision
If complex data relationship mapping is implemented to improve data analysis accuracy, then semantic understanding improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the data relationship modeling into distinct, manageable components such as entities, attributes, relationships, and constraints. Each component is defined and managed independently, allowing complex data relationships to be built from simpler building blocks. This segmentation enables accurate representation of data relationships while keeping the system complexity manageable through modular design and clear separation of concerns.
4Adaptability or versatility
If traditional interfaces are used for audience building, then the process is simple, but flexibility and adaptability are limited
Solution Approach 1:
The patent creates a universal audience building interface that can handle multiple types of queries, data sources, and analysis requirements through a single unified system. The interface provides flexible querying capabilities while maintaining ease of use through standardized operations and consistent interaction patterns. This multi-functional approach allows users to build diverse audiences without learning multiple specialized tools, balancing adaptability with operational simplicity.
Data Source
AI summary
Methods and systems for providing semantic meaning to data items in a data warehouse are disclosed. A data graph specification written in a configuration language is received. The data graph specification defines a plurality of data entities and relationships between the data entities. The received data graph specification is parsed to generate an object representation of the data graph. A schema of a data warehouse is validated against the object representation of the data graph. One or more queries based on the object representation of the data graph.