Predictive Data Aggregation for Granular OLAP Analysis
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Solution Overview
Problem
On-Line Analytical Processing (OLAP) primarily analyzes past trends and relationships in enterprise data, failing to predict future trends and reduce data granularity effectively, limiting its ability to break down contributions of causal data on dependent data.
Innovation Solution
A method and apparatus for predicting multi-dimensional dependent data using a predictive model based on historical and anticipated causal data, allowing for the generation of non-measurable data and enabling detailed future predictions with granular analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If OLAP pre-calculates and aggregates enterprise data into larger aggregations to reduce query time, then the amount of time involved in analyzing past trends is reduced, but the data granularity is lost and future trend prediction capability is limited
Solution Approach 1:
The patent segments the data analysis process into distinct components: OLAP handles aggregated historical data analysis while the predictive model handles future trend predictions at granular levels. This segmentation allows each component to operate at its optimal granularity without compromising the other.
Solution Approach 2:
The system performs preliminary actions by pre-calculating both aggregated historical data (via OLAP) and predictive model parameters. This preliminary calculation enables rapid querying of both past trends and future predictions without compromising granularity in the predictive component.
2Loss of information
If OLAP analyzes past trends using historical data, then insights into historical relationships are obtained, but the ability to predict future trends is failed
Solution Approach 1:
The patent creates a multi-functional system where OLAP provides historical analysis capabilities while the predictive model layer adds future prediction capabilities. The system universally handles both historical querying and future forecasting, making the overall system adaptable to both past analysis and future planning needs.
Solution Approach 2:
The predictive model acts as an intermediary layer between the historical OLAP data and future trend requirements. It takes historical data and causal factors as input and produces predictive outputs, mediating between past analysis capabilities and future prediction needs.
3Productivity
If OLAP reduces dependent data granularity by aggregating data, then query performance is improved, but the ability to break down contributions of causal data on dependent data is limited
Solution Approach 1:
The patent adds another dimension to the data analysis by introducing predictive variables and causal factors as separate analytical dimensions. This allows the system to maintain aggregated views for performance while adding predictive granularity through additional dimensions without compromising query speed.
Data Source
AI summary
Methods and apparatuses for predicting set of multi-dimensional dependent data and non-measurable data from a set of multi-dimensional historical dependent and causal data are described. In one embodiment, the method comprises receiving input data that comprises multi-dimensional historical dependent data and causal data and anticipated activity data, determining a set of multi-dimensional predicted dependent data using a predictive model and the input data, creating non-measurable data based on the set of multi-dimensional predicted dependent data and the input data.