Database Architecture for Data Value Optimization
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Solution Overview
Problem
Current database architectures face challenges in optimizing and managing large-scale data processing and analytics, particularly in accurately adjusting data values based on forecasts and seasonality factors to achieve optimal business outcomes.
Innovation Solution
A system and method for executing a data value adjustment routine that includes segmenting data, calculating performance metrics, applying clustering algorithms, and adjusting data values using executable rules to generate adjusted data values, which are then used to forecast impacts and update datasets based on user-selected criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data values are adjusted using traditional database architectures, then data management capability is maintained, but accuracy of data value adjustments and forecasts deteriorates
Solution Approach 1:
The patent segments the database architecture into multiple specialized components: a data lake for raw data storage, a data warehouse for processed data, a forecasting engine for predictive analytics, and a data mart for targeted data delivery. This segmentation allows each component to specialize in specific functions, improving the accuracy of data value adjustments while distributing the overall system complexity across manageable modules rather than concentrating it in a single database system.
Solution Approach 2:
The patent introduces a forecasting engine as an intermediary component between the data warehouse and the data mart. This forecasting engine acts as a mediator that processes raw data, applies seasonal factors and trends, generates adjusted data values, and then delivers results to the data mart. This intermediary layer improves measurement precision by dedicating specific computational resources to forecasting operations, while the modular architecture prevents the complexity from overwhelming the entire system.
2Productivity
If traditional database systems are used for large-scale data processing, then system simplicity is maintained, but productivity in data analytics deteriorates
Solution Approach 1:
The patent divides the data processing system into distinct segments: data lake for ingestion, data warehouse for storage and initial processing, forecasting engine for advanced analytics, and data mart for distribution. This segmentation enables parallel processing across multiple components, significantly improving productivity for large-scale data analytics. Each segment can be optimized independently, and the modular structure manages complexity by allowing teams to work on individual components without affecting the entire system.
Solution Approach 2:
The patent implements preliminary action by pre-processing and pre-aggregating data in the data warehouse before it reaches the forecasting engine. Seasonal factors, trends, and other analytical parameters are pre-calculated and stored, so when analytics queries are executed, the forecasting engine can quickly retrieve and process pre-prepared data rather than computing everything from scratch. This preliminary preparation dramatically improves processing efficiency while the modular architecture keeps the complexity manageable through clear separation of concerns.
3Measurement precision
If data is not segmented and standardized, then data storage simplicity is maintained, but measurement precision of performance metrics deteriorates
Solution Approach 1:
The patent segments data into different categories within the data warehouse: raw data, cleaned data, aggregated data, and standardized data. Each segment undergoes specific processing appropriate to its stage. Performance metrics are calculated from standardized data segments, ensuring measurement precision. The segmentation approach manages complexity by organizing data processing into discrete, manageable stages rather than attempting to process all data uniformly.
Solution Approach 2:
The patent applies parameter changes by transforming raw data into standardized formats with consistent data types, units, and structures. The forecasting engine modifies data parameters by applying seasonal factors, trend adjustments, and other transformations to generate adjusted data values. These parameter changes are systematically applied through the modular architecture, improving metric accuracy while the step-by-step transformation process keeps complexity manageable through clear data flow and validation at each stage.
Data Source
AI summary
Embodiments of an architecture for large scale key factor optimization and management are disclosed. The architecture may include an optimization and management system that uses existing data to characterize items, group them based on user selected similarity criteria and/or user-selected objectives for the performance metrics, further allowing the user to define target objectives for each group and through forecasting of the impact of the key factor value changes, select the adjusted key factor values that better achieve the selected objectives.