Semantic Smart Cache for Multi-Source Data Integration
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Solution Overview
Problem
Current data access and integration technologies lack effective methods to virtualize data from multiple data stores and serve it to consumers, leading to complexity and inefficiency in data access and usage.
Innovation Solution
A semantically driven smart cache system that automatically ingests diverse data sources, structures, organizes, and optimizes data based on semantic models, generating scalable and stable service endpoints to provide data to recipient systems without altering the original data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data is accessed directly from multiple diverse data sources, then data access complexity increases, but data integration capability is insufficient
Solution Approach 1:
The patent introduces a smart cache as an intermediary layer between diverse data sources and consuming applications. This smart cache automatically ingests data from multiple data sources, reconciles it using semantic models, and provides unified access points. The intermediary handles the complexity of data integration internally while presenting a simplified interface to applications, thus reducing data access complexity without compromising integration capability.
Solution Approach 2:
The patent segments the data integration function into distinct components: data ingestion module, semantic model updating module, classification module, and service endpoint generation module. Each component handles a specific aspect of data integration, making the overall system more manageable and less complex while maintaining high adaptability through modular design.
2Manufacturing precision
If semantic models are continuously updated to reconcile diverse data, then data accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-ingesting data from data sources and pre-updating semantic models before actual data access requests. The smart cache proactively reconciles data and generates service endpoints in advance, so when applications need data, it is already processed and ready for rapid retrieval, thus maintaining high accuracy without adding processing delay during actual access.
Solution Approach 2:
The patent implements continuous data ingestion and semantic model updating in the background without interrupting data access operations. The smart cache maintains continuous synchronization with data sources while simultaneously serving application requests, ensuring data accuracy is continuously improved without causing time loss in data retrieval operations.
3Ease of operation
If service endpoints are generated to simplify data access, then ease of operation improves, but system scalability may be limited
Solution Approach 1:
The patent implements dynamic service endpoint generation that automatically adapts to new data sources and data changes. The smart cache continuously monitors data sources, detects changes, and dynamically generates or updates service endpoints without manual intervention. This dynamic approach maintains ease of operation while ensuring the system can scale to accommodate new data sources and evolving requirements.
Solution Approach 2:
The patent creates universal service endpoints that can serve multiple data sources and multiple consuming applications through a single unified interface. The smart cache generates scalable service endpoints that provide consistent data access patterns regardless of the underlying data source diversity, enabling the system to scale horizontally by adding new data sources without increasing operational complexity for applications.
Data Source
AI summary
A method of integrating data across multiple data stores is provided. The method includes ingesting diverse data from multiple data sources and reconciling the ingested diverse data by updating semantic models based on the ingested diverse data. The method further includes storing the ingested diverse data based on one or more classification of the data sources according to the semantic models and automatically generating scalable service endpoints that are semantically consistent according to the classification of the data sources. The generated scalable service endpoints are application programming interfaces. The method also includes determining a protocol based on the scalable service endpoints in response to receiving a call from the one or more recipient systems and responding to the call from the one or more recipient systems by providing data in the classification of the data sources.