Integration Objects for Data Retrieval in Massive Warehouses
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Solution Overview
Problem
Massive data warehouses face challenges in efficient data access, leading to high CPU load and performance issues when accessing large amounts of data simultaneously, as existing systems are optimized for long-term storage rather than specific data usage.
Innovation Solution
Integration objects are introduced to enhance data retrieval by mapping data objects from multiple sources into integration data objects, allowing for shared access and optimized sequencing relationships, which reduce execution time, memory, CPU, and network usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is accessed directly from massive data warehouses, then data retrieval can be performed, but CPU load increases and system performance decreases
Solution Approach 1:
The patent introduces integration objects as intermediary components between applications and data sources. These integration objects cache data from multiple data sources and provide a unified access interface, reducing direct queries to the underlying data warehouses. This mediator layer filters and prepares data before it reaches the application, thereby reducing CPU load on the data warehouse system while maintaining data retrieval functionality.
2Productivity
If data is accessed directly from massive data warehouses, then data can be retrieved, but system performance decreases due to resource intensity
Solution Approach 1:
The integration objects perform preliminary actions by pre-fetching and caching data from multiple data sources before actual application access is needed. The system proactively loads data into the integration object's memory structure, so when applications request data, it is already prepared and available. This eliminates the need for time-consuming direct queries to the data warehouse at the moment of access, significantly reducing execution time.
3Quantity of substance
If data warehouses are designed for long-term storage, then storage capacity is optimized, but specific data usage requirements are not met
Solution Approach 1:
The integration objects apply local quality by creating customized data views tailored to specific application requirements. Instead of forcing applications to adapt to the data warehouse's storage structure, the integration objects transform and present data in formats optimized for specific use cases. Each integration object can be configured with different data transformations, filters, and access patterns suited to particular application needs, while the underlying data warehouse maintains its storage-optimized structure.
Data Source
AI summary
Techniques and solutions are described for providing integration objects that can be used to enhance data retrieval, and can be used to provide shared data access for multiple applications, and to allow access to application functionality through an interface. The integration object includes a plurality of integration data objects, at least a portion of which are mapped to data objects stored in one or more data sources. The integration data objects can hold values stored in correspondingly mapped locations of the data objects, such as in a value field. A reference field of the integration data objects can hold reference information regarding a location of a value in a respectively mapped data source. At least a portion of the integration data objects can include a defined sequencing with one or more other integration data objects.