Analytics Server Star Schema Cost Recalculation
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Solution Overview
Problem
The process of providing business intelligence data is highly manual and resource-intensive, requiring significant expertise and time, especially when changes are made to data structures or report requirements, leading to inefficiencies and potential human errors.
Innovation Solution
A system and method utilizing an analytics server that aggregates data from multiple sources, generates dimensions and measures, and stores relationship information in a snowflake or star schema, enabling efficient calculation and recalculation of total product costs based on changes in sub-costs, thereby reducing the need for extensive manual recoding and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual extraction and transformation functions are used to provide business intelligence data, then data can be extracted and transformed from source systems, but the process becomes highly manual intensive and requires significant expertise and time
Solution Approach 1:
The patent applies preliminary action by pre-defining extraction and transformation functions that can be automatically applied when source system changes are detected. Instead of manually recoding functions when changes occur, the system has pre-configured functions that can be automatically activated or adjusted based on detected changes in source systems, thereby reducing the time and expertise required for manual recoding.
Solution Approach 2:
The patent implements feedback by monitoring source system changes and automatically triggering updates to extraction and transformation functions. The system detects changes in source systems and feeds this information back to automatically adjust the ETL processes, eliminating the need for manual intervention and significantly improving productivity while reducing the time loss associated with manual recoding.
2Adaptability or versatility
If manual recoding is performed to adapt to changes in source system data structure or report requirements, then the system can accommodate changes, but the effort required becomes substantial and is very sensitive to human error
Solution Approach 1:
The patent applies self-service by enabling the system to automatically detect, adapt to, and correct itself when source system changes occur. The system monitors source systems for changes and automatically adjusts extraction and transformation functions without human intervention, thereby maintaining high adaptability while eliminating human error completely. The system serves itself by autonomously managing its own configuration and adaptation.
Solution Approach 2:
The patent replaces the mechanical manual recoding process with an automated electronic system that detects source system changes and automatically updates extraction and transformation functions. This substitution eliminates human involvement in the recoding process, maintaining full adaptability to changes while removing the sensitivity to human error that plagues manual processes.
3Loss of information
If comprehensive data extraction and transformation functions are designed to meet all reporting requirements, then complete business intelligence data can be provided, but the entire process takes from two to six months
Solution Approach 1:
The patent applies preliminary action by pre-configuring extraction and transformation functions that can handle multiple reporting requirements simultaneously. Instead of developing functions incrementally over months, the system has pre-defined functions that can be automatically activated based on detected source system changes, thereby providing complete business intelligence data much faster while maintaining information completeness.
Solution Approach 2:
The patent implements continuity of useful action by establishing an automated, continuous process that monitors source systems and continuously updates extraction and transformation functions as changes are detected. This eliminates the lengthy batch processing approach and enables the system to continuously provide complete business intelligence data without the months-long development cycles associated with manual function design and testing.
Data Source
AI summary
A computer implemented method for data mining and providing business intelligence data including generating by an analytics server one or more dimensions from source data imported from a computer readable medium, wherein the one or more dimensions define categories into which portions of the normalized data can be grouped; generating by the analytics server one or more measures from the source data linked to the one or more dimensions; storing by the analytics server the one or more dimensions and the one or more measures in a plurality of tables arranged in one of a snowflake and a star schema; determining by the analytics server relationship information between one or more measures and one or more dimensions in each of the plurality of tables; storing by the analytics server the relationship information on the computer readable medium; calculating by the analytics server a total cost of at least one product based on the relationship information; and, querying by a computer system in communication with the analytics server for the change in total cost of the at least one product based on a change in any one of the measures.