Multiple Logical Data Models for Storage System Adaptability
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Solution Overview
Problem
Current data management systems typically rely on a single logical data model to map to a physical data model or storage system, limiting flexibility and scalability, especially in handling diverse vertical domains and large volumes of dynamic data.
Innovation Solution
Implementing multiple logical data models that expose a data storage system using semantic mapping sets, allowing different modelling notations and accounting for the lifecycle of these models, including birth, retirement, merging, and splitting, while using a common notation for communication with the physical data model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single logical data model is used to map to the physical data model, then the system structure is simple, but the flexibility and scalability are limited
Solution Approach 1:
The patent divides the single logical data model into multiple logical data models (first logical data model, second logical data model, etc.), each serving different vertical domains. This segmentation allows each model to be independently designed and optimized for specific domains while maintaining overall system flexibility and scalability.
Solution Approach 2:
The patent introduces a new dimension by adding multiple logical data models layered between the physical data model and application layers. This dimensional expansion enables the system to handle diverse vertical domains without increasing physical storage complexity, resolving the contradiction between flexibility and structural simplicity.
2Adaptability or versatility
If multiple logical data models are implemented to serve different vertical domains, then adaptability improves, but system complexity increases
Solution Approach 1:
The patent creates a universal mapping mechanism that can handle multiple logical data models through a common physical data model interface. The mapping relationships establish a standardized way to interact with the underlying storage system, allowing different logical models to serve various domains while sharing common infrastructure, thus managing complexity.
Solution Approach 2:
The patent introduces mapping relationships as intermediaries between logical data models and the physical data model. These mappings act as mediators that translate between different logical models and the unified physical storage layer, enabling adaptability across domains while abstracting away the complexity of direct interactions.
3Adaptability or versatility
If a single data model structure is used, then the system is easier to manage, but handling diverse vertical domains becomes difficult
Solution Approach 1:
The patent segments the data management system into multiple logical data models, each optimized for specific vertical domains (e.g., different business domains or application areas). This segmentation allows domain-specific optimizations while maintaining ease of management through standardized mapping mechanisms to the physical layer.
Solution Approach 2:
The patent applies local quality by allowing each logical data model to have domain-specific characteristics and optimizations tailored to its vertical domain, while the underlying physical data model and mapping mechanisms maintain standardized management practices. This enables diverse domain coverage without sacrificing overall manageability.
4Ease of operation
If multiple logical data models are used to expose the data storage system, then data access flexibility improves, but the complexity of managing model lifecycles increases
Solution Approach 1:
The patent establishes mapping relationships that create feedback loops between logical data models and the physical data model. These mappings enable the system to track and manage the lifecycle of logical models (creation, modification, retirement) by monitoring changes in the underlying physical structure, thus improving data access flexibility while providing mechanisms to manage complexity.
Data Source
AI summary
The use of multiple logical data models to expose a data storage system. Each logical data model may expose the data storage system using a semantic mapping set that maps sets of entities or attributes of the respective logical data model to corresponding sets of entities or attributes of the physical data model or perhaps directly to the data storage system itself. Each logical data model might serve a different vertical, and have a particular modelling notation selected by the logical data model provider. The mapping may also translate different logical modelling notations into a common logical modelling notation for use in communicating with the physical data model. The system may account for the lifecycle of the logical data model including birth or retirement of logical data model entities, and merging or splitting of logical data models.