Automated Data Warehouse Customization via Usage Pattern Analysis
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Solution Overview
Problem
The manual process of custom data warehouse design, generation, and population in Business Intelligence environments is time-consuming and costly, especially for Software as a Service providers serving multiple customers, as it requires specialized ETL developers to gather user requirements and perform laborious tasks such as ETL job scheduling and data synchronization.
Innovation Solution
Automated techniques analyze data usage patterns to tailor generic domain-specific data warehouses and data marts, prioritizing jobs and generating data marts based on user access patterns, allowing for on-the-fly analysis and synchronization across prepackaged applications, reducing the need for manual intervention and custom development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual custom data warehouse design and ETL development is performed by specialists, then data warehouse functionality and user requirements are met, but development time and costs increase significantly
Solution Approach 1:
The patent uses templates to copy proven data warehouse designs and ETL patterns for common scenarios. Instead of manually designing from scratch, the system provides pre-configured templates that can be instantiated and customized, dramatically reducing development time while maintaining adaptability to user needs through template selection and parameter customization.
Solution Approach 2:
The system enables automated self-service through usage pattern analysis. By monitoring how users actually interact with data warehouses, the system automatically generates optimized ETL jobs and data mart configurations without requiring manual specialist intervention. This allows the system to adapt to user requirements autonomously based on observed usage patterns.
2Reliability
If manual ETL job scheduling and data synchronization is performed, then data accuracy and freshness are maintained, but labor costs and operational complexity increase
Solution Approach 1:
The system implements feedback loops by continuously monitoring usage patterns and automatically adjusting ETL job schedules and data synchronization frequencies. Usage data feeds back into the system to optimize when data is refreshed and how it is synchronized, maintaining data accuracy while reducing manual operational complexity through automated adaptive scheduling.
Solution Approach 2:
The system performs self-service by automatically generating and scheduling ETL jobs based on analyzed usage patterns. Instead of requiring manual configuration and monitoring of complex synchronization tasks, the system autonomously manages data extraction, transformation, and loading operations, reducing operational complexity while maintaining reliability.
3Productivity
If generic prepackaged data warehouses are used without customization, then deployment speed increases, but adaptability to specific user needs decreases
Solution Approach 1:
The system transitions from static generic warehouses to dynamic customized data marts by automatically generating specialized data structures based on observed usage patterns. The system starts with generic prepackaged warehouses for rapid deployment, then dynamically adapts and customizes data marts for specific users and use cases based on how they actually use the data, achieving both speed and adaptability.
Solution Approach 2:
The system segments the generic data warehouse into specialized data marts tailored to specific users and use cases. By dividing the monolithic generic warehouse into smaller, purpose-specific data marts based on usage patterns, the system maintains the rapid deployment advantage of generic templates while achieving the adaptability of customized solutions for specific user needs.
4Adaptability or versatility
If comprehensive data is stored in data warehouses, then query flexibility and analysis capabilities are improved, but data storage costs and processing time increase
Solution Approach 1:
The system extracts only the necessary data subsets from the comprehensive data warehouse based on analyzed usage patterns. By identifying which data elements, tables, and relationships are actually used by specific users and use cases, the system extracts and creates focused data marts that contain only the relevant data, reducing storage volume and processing requirements while maintaining query flexibility for the intended purposes.
Data Source
AI summary
Approaches for a user-driven warehousing approach are provided, wherein usage patterns for business intelligence applications are gathered, for example in an automated recording fashion, allowing the automated scheduling of jobs in a manner that prioritizes jobs that populate the most-used tables and scheduling those jobs in a manner to ensure that the data is up-to-date prior to when it is generally accessed. The usage pattern analysis also allows for the automated identification of more focused data marts for particular situations. The usage pattern analysis also provides for automated data warehouse/data mart creation and customization based on usage patterns that may be used as a seed, as well as for on-the-fly latitudinal analysis across prepackaged domain-specific applications.