Data Access Framework Decoupling Analytics From Storage
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Solution Overview
Problem
Current systems for providing sensor data to cloud computing environments require tight coupling between analytics and data storage structures, making them unwieldy and limiting the portability and reusability of analytics and predictive models across different asset installations.
Innovation Solution
A data access framework that decouples analytics from underlying asset data stores by using data access layers, allowing for modular extraction, formatting, and access of asset information through a logical system model, enabling predictive models to access data without needing to understand the underlying storage schema.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If analytics are tightly coupled to asset data storage structures, then data access is straightforward, but system complexity increases and portability decreases
Solution Approach 1:
The patent introduces a data access layer as an intermediary component between analytics and asset data stores. This layer provides standardized access methods and abstractions, allowing analytics to query data without directly interacting with complex storage schemas. The mediator handles data retrieval, transformation, and presentation, thereby simplifying analytics operations while managing storage complexity centrally.
Solution Approach 2:
The system is segmented into distinct layers: the asset data storage layer, the data access layer, and the analytics layer. Each layer has specific responsibilities and interfaces with adjacent layers through defined contracts. This segmentation allows each component to be developed, maintained, and modified independently, reducing overall system complexity while maintaining operational simplicity.
2Productivity
If analytics are tightly coupled to specific asset data stores, then data retrieval is efficient, but adaptability to different assets decreases
Solution Approach 1:
The data access layer implements universal access patterns and standardized interfaces that work across different asset types and data store configurations. By providing a unified access mechanism that can adapt to various underlying storage systems, the analytics can operate efficiently on diverse assets without requiring asset-specific customization, thereby achieving both efficiency and versatility.
Solution Approach 2:
The system creates abstract representations or views of asset data through the data access layer, which replicate the essential data structures and access patterns needed by analytics. These abstract copies allow analytics to operate on standardized data representations rather than directly on diverse physical storage systems, enabling portability while maintaining efficient data retrieval through optimized access paths.
3Adaptability or versatility
If a unified data access framework is implemented, then analytics portability improves, but initial system complexity increases
Solution Approach 1:
The data access layer and its access patterns are designed and implemented in advance, before analytics are deployed. This preliminary establishment of standardized access mechanisms creates a ready-to-use framework that analytics can immediately utilize. The upfront investment in creating the access layer simplifies subsequent analytics deployment and enhances portability, as analytics inherit the portability benefits from the pre-built framework.
Solution Approach 2:
The system allows configuration parameters and metadata to define asset-specific properties, data store characteristics, and access patterns. By parameterizing the data access framework rather than hardcoding asset-specific logic, the system achieves flexibility and portability. Analytics can adapt to different assets by utilizing these parameters, reducing the need for complex customizations while maintaining a unified framework.
Data Source
AI summary
Some aspects are directed to systems for providing access to data. An example includes a computer system implementing a data access framework for providing data to one or more predictive models. The system is configured to receive at least one asset definition comprising an asset identifier for at least one asset associated with at least one data source, receive at least one data lens definition comprising a data lens identifier and a logical model identifier, the logical model identifier identifying at least one portion of a logical system model, access a first datastore to retrieve asset information extracted from the at least one data source, determine, using the logical model identifier, a portion of the logical system model associated with the retrieved asset information, format the retrieved asset information for storage in a second datastore corresponding to the logical system model, and store the formatted retrieved asset information in the second datastore.