Pattern Significance Ranking in Multi-Dimensional Data Analysis
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Solution Overview
Problem
In multi-dimensional databases, identifying and comparing the significance of various data patterns such as outliers, step changes, and trends is challenging due to the vast number of time series and disparate types of patterns, which hinders effective decision-making.
Innovation Solution
A computer method and system that calculates a relative pattern significance factor by determining percentage changes for outliers, step changes, and trends, using metrics like median, inter-quartile range, and statistical regression, to provide a ranked list of pattern types based on their significance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple types of data patterns (outliers, step changes, trends) are analyzed in multi-dimensional databases, then the comprehensiveness of pattern identification is improved, but the difficulty of comparing and determining significance of different pattern types increases
Solution Approach 1:
The patent transforms different pattern types (outliers, step changes, trends) into a common significance metric by calculating standardized statistical measures. Each pattern type is evaluated using parameter transformations that convert diverse patterns into comparable significance scores, enabling unified ranking and comparison across all pattern types.
Solution Approach 2:
The patent introduces an intermediary significance metric that acts as a mediator between different pattern types. This intermediary measure standardizes the evaluation of outliers, step changes, and trends, allowing them to be compared on a common scale without directly comparing their inherently different characteristics.
2Ease of operation
If a common measure is introduced to compare disparate pattern types, then the ease of determining pattern significance is improved, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the analysis process into distinct modules: outlier detection module, step change detection module, trend detection module, and significance calculation module. Each module handles a specific pattern type independently, processing data through specialized algorithms before consolidating results into a unified significance ranking, thereby managing complexity through modular design.
Solution Approach 2:
The patent applies parameter transformations to convert diverse pattern characteristics into a standardized significance metric. By changing the parameters of different pattern types into a common evaluation framework, the system simplifies the comparison process while maintaining the ability to handle multiple pattern types through systematic parameter standardization.
Data Source
AI summary
Disclosed is a computer method and system for identifying significance of patterns across a plurality of data patterns, which involves identifying pattern types of the plurality of data patterns, determining a relative pattern significance factor to compare the pattern types. Determining the relative pattern significance factor further involves calculating a percentage change of an identified outlier from a median for a outlier pattern, calculating a value of a step change as a percentage of a last value of a step preceding the step change for a step change pattern and calculating a percentage change from a start value on the fitted curve to an end value on the fitted curve for a trend pattern. A ranked list of the pattern types are returned based on their corresponding relative pattern significant factors.