Automated Dataset Join Configuration for Accurate Report Aggregation
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Solution Overview
Problem
Existing systems fail to provide intuitive and user-friendly tools for generating data reports that accurately synthesize and summarize data across multiple disparate datasets, leading to siloed and inconsistent organizational information due to the isolation of different applications and systems.
Innovation Solution
A computing system that automatically determines join configurations for combining multiple datasets, generates a combined dataset, assigns unique identifiers, performs aggregation operations, and generates reports based on these operations, providing customizable and efficient data report generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual updates and bespoke software integrations are used to maintain accuracy and consistency of organizational information, then data accuracy can be maintained, but system complexity and time consumption increase significantly
Solution Approach 1:
The system automatically determines join configurations and generates combined datasets by self-service, eliminating the need for manual updates and bespoke software integrations. The automated join configuration system analyzes data schemas, identifies relationships, and generates integration logic without human intervention, thereby maintaining data accuracy while reducing system complexity
Solution Approach 2:
The system performs preliminary actions by pre-defining join configurations and data relationships in advance. By automatically analyzing data schemas and establishing join logic before data integration is needed, the system prepares the integration framework beforehand, reducing the complexity of ongoing manual maintenance while ensuring data accuracy
2Reliability
If manual updates and bespoke software integrations are used to maintain consistency across applications, then data consistency can be maintained, but time consumption increases
Solution Approach 1:
The automated join configuration system performs self-service by automatically determining how to join datasets, generating integration logic, and maintaining data consistency across applications without requiring manual intervention. This eliminates the time-consuming nature of manual updates while preserving data consistency through automated enforcement of join configurations
Solution Approach 2:
The system establishes continuous automated data integration processes that continuously maintain data consistency across applications. By implementing ongoing automated join operations and data synchronization, the system eliminates the need for repeated manual updates, thereby reducing time consumption while maintaining consistent organizational information across all systems
3Productivity
If automated join configuration is implemented to reduce manual effort, then productivity improves, but computational resources increase
Solution Approach 1:
The system applies partial action by implementing automated join configuration only for the specific data integration tasks that require it, rather than automating all possible system operations. This selective automation approach improves productivity for report generation while limiting computational resource consumption to only the necessary automated configuration processes, avoiding excessive resource usage
Data Source
AI summary
A computing system includes computer-readable media to store instructions and processor(s) to execute the instructions to perform operations including: receiving a request for generation of a report having a plurality of data elements; identifying two or more different datasets that collectively contain the plurality of data elements; automatically generating one or more join configurations for combining the two or more different datasets based on a prioritization of data elements which define a join key for the one or more join configurations; executing the one or more join configurations to generate a combined dataset from the two or more different datasets; assigning unique identifier(s) to a portion of the combined dataset and expanding the combined dataset; performing an aggregation operation associated with the portion of the combined dataset by referencing the unique identifier(s); and generating the report based on the combined dataset and the aggregation operation.