Dynamic Data Mapping for Recommendation Objects
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Solution Overview
Problem
Current systems for generating recommendations in cloud computing environments require data duplication across multiple databases, leading to inefficiencies and increased resource usage, particularly when dealing with large datasets, and do not allow for real-time updates to recommendation objects.
Innovation Solution
The system dynamically maps data from separate data sources to recommendation objects without duplicating data, enabling real-time or near real-time updates and allowing users to build recommendation strategies graphically without coding, by utilizing process flows and predictive models to determine when and what content to present.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is replicated between databases to enable distributed data access, then data availability for recommendation generation is improved, but network and computing resources are consumed excessively
Solution Approach 1:
The patent extracts only the necessary data elements from remote systems and brings them locally through data virtualization, rather than replicating entire datasets. This allows recommendation generation to access distributed data without the overhead of full data replication, reducing network and computing resource consumption while maintaining data availability.
Solution Approach 2:
The patent introduces a data virtualization layer as an intermediary between the recommendation engine and distributed data sources. This mediator enables access to remote data without physical replication, allowing the system to query and process distributed data efficiently while minimizing network traffic and computational overhead.
2Reliability
If data is replicated between databases to enable distributed data access, then data availability for recommendation generation is improved, but data synchronization problems are introduced
Solution Approach 1:
The patent extracts only the specific data needed for recommendations from remote systems rather than replicating entire datasets. This selective extraction approach eliminates the synchronization complexity that arises from maintaining copies of large datasets across multiple databases, while still providing the necessary data for recommendation generation.
Solution Approach 2:
The data virtualization layer acts as an intermediary that manages access to distributed data sources without requiring data replication. This mediator handles data retrieval and coordination transparently, eliminating the need for complex synchronization mechanisms while maintaining data availability for recommendations.
3Adaptability or versatility
If data duplication is used to enable recommendation generation from distributed data sources, then access to distributed data is improved, but storage efficiency is reduced
Solution Approach 1:
The patent extracts only the essential data elements from distributed sources that are needed for recommendation generation, rather than duplicating entire datasets. This selective extraction maintains adaptability to access distributed data while minimizing the quantity of stored data, improving storage efficiency.
Solution Approach 2:
The data virtualization layer serves as an intermediary that enables access to distributed data without requiring local duplication. This approach maintains the versatility to work with distributed data sources while avoiding the storage overhead of data duplication, optimizing storage efficiency.
Data Source
AI summary
System and methods are described for generating recommendations from dynamically-mapped data. In one implementation, a database system receives a first request to generate a recommendation objection and a second request to retrieve additional data to include in the recommendation object. The database system retrieves the recommendation data from a first database table. The database system identifies the additional data in a second database table that is stored separately from the first database table. The database system generates the recommendation object to include the recommendation data from the first database, and maps the additional data to one or more fields of the recommendation object.