Consistent Semantic Layer for Selective Data Indexing
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Solution Overview
Problem
Traditional data management systems struggle to efficiently process and manage large volumes of heterogeneous data, leading to resource-intensive processing, scalability issues, and inefficiencies in data retrieval and analysis.
Innovation Solution
The implementation of a semantic layer within data management systems that utilizes predictive modeling and large language models to provide a consistent semantic context across data management aspects, allowing for dynamic data ingestion, selective indexing, and context-driven data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If all data is indexed to enable search and analysis, then data retrieval efficiency is improved, but processing overhead and resource consumption increase
Solution Approach 1:
The patent segments data into different types (structured, semi-structured, unstructured) and applies different indexing strategies to each. Structured data receives full indexing for fast retrieval, while unstructured data uses selective or no indexing, reducing overall processing overhead while maintaining retrieval efficiency for commonly accessed data.
Solution Approach 2:
The system applies different quality levels of indexing to different data portions based on their characteristics and access patterns. High-value structured data receives comprehensive indexing with full semantic enrichment, while lower-priority unstructured data receives minimal or no indexing, optimizing the balance between retrieval speed and processing resources.
2Ease of operation
If data is processed into a searchable format with late-binding schemes, then data can be searched, but the architecture becomes inefficient for queries across multiple indexes
Solution Approach 1:
The patent merges multiple data indexes into a unified semantic layer that uses a consistent schema. This allows queries to span across multiple data sources and index types simultaneously without requiring separate search operations, significantly improving query efficiency while maintaining full search capability across all data.
Solution Approach 2:
The unified semantic layer serves multiple functions: it provides a consistent data model for querying, enables cross-index searches, supports both structured and unstructured data, and facilitates analytics operations. This multi-functional approach eliminates the inefficiencies of separate indexing schemes while enhancing overall system productivity.
3Speed
If early binding schemes are used to organize data based on predefined rules, then data retrieval efficiency is improved, but the system becomes inflexible and requires manual adjustments
Solution Approach 1:
The patent implements a dynamic schema mapping system that automatically adapts to new data formats and structures. The semantic layer uses configurable mapping rules that can be adjusted without manual intervention, allowing the system to maintain fast retrieval speeds through structured organization while simultaneously adapting to changing data requirements and new data sources.
Solution Approach 2:
The system changes the parameters of data organization dynamically based on data type, access patterns, and query requirements. The semantic layer can transform data between different representations and apply different indexing strategies on-the-fly, maintaining retrieval efficiency while adapting to diverse and evolving data needs without requiring rigid predefined structures.
4Power
If increasing amounts of compute capabilities are provided for data management, then data processing capacity is improved, but resource scarcity and cost increase
Solution Approach 1:
The patent applies partial indexing and processing to only the portions of data that are most frequently accessed or most valuable. Rather than processing all data uniformly, the system selectively applies compute resources to high-priority data segments, maintaining processing capacity for critical operations while reducing overall resource consumption for less important data.
Solution Approach 2:
The system dynamically adjusts processing parameters such as indexing depth, semantic enrichment level, and query optimization intensity based on data characteristics and system load. This allows the data management system to maintain high processing capacity when needed while reducing resource consumption during normal operations, optimizing the balance between power and quantity of resources used.
Data Source
AI summary
Embodiments may provide a robust, efficient, adaptable, and scalable mechanism for simply and efficiently ingesting data to contextualize data during ingest based on a semantic layer. This context (e.g., and the semantic layer) may then be utilized to index only the portions of the data that need be indexed, or to extract, process and structure only data that needs to be extracted and structured based on actual use cases for that data (e.g., while also structuring that data according to structures that may be tailored to those use cases). Consequently, as needs for data change or the data itself changes embodiments may easily adapt to this new data or new use cases, as the indexing and structuring of data in embodiments is based on the context of such data as determined by the semantic layer during ingest.