Time Dimension Ranking via Quality and Informativeness Factors
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Solution Overview
Problem
Existing methods for ranking time dimensions in datasets struggle with handling missing values and varying data quality, often requiring uniform data quality and assuming equal importance of all time points, which limits their ability to identify informative changes, especially recent changes.
Innovation Solution
The system converts date dimensions into time series, calculates a value quality factor and a time series informative factor, and assigns weights based on data quality and recency, allowing for ranking of time dimensions within a specified time window, even with missing values, to identify more informative time series.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If existing methods use uniform data quality requirements for all time dimensions, then data consistency is improved, but the ability to handle missing values and rank time dimensions with varying data quality deteriorates
Solution Approach 1:
The patent applies local quality by introducing a value quality factor that varies for each time dimension based on its specific data characteristics. Instead of enforcing uniform data quality across all time dimensions, the system calculates individual quality scores considering missing values, data density, and other quality metrics for each time dimension separately. This allows the system to adaptively handle varying data quality levels across different time dimensions while maintaining overall analytical consistency.
2Device complexity
If existing methods assume equal importance of all time points, then simplicity is improved, but the ability to identify informative changes especially recent changes deteriorates
Solution Approach 1:
The patent implements dynamics by introducing a time series informative factor that dynamically weights different time points based on their informativeness. The system calculates this factor using metrics such as value changes, volatility, and recency, allowing more informative time points (especially recent changes) to have higher weights. This dynamic weighting mechanism enables the system to identify significant changes without requiring complex manual intervention, balancing simplicity with information retention.
3Productivity
If existing methods rank time dimensions without considering data quality and recency, then computational efficiency is improved, but the accuracy of identifying insightful activities deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming the ranking mechanism to incorporate multiple quality parameters including value quality factor and time series informative factor. The system calculates composite scores that combine these parameters with configurable weights, allowing flexible adjustment of ranking criteria. This approach maintains computational efficiency through algorithmic optimization while significantly improving ranking accuracy by considering data quality and recency parameters that were previously ignored.
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for ranking time dimensions. One example method includes receiving a request for an insight analysis for a dataset that includes a value dimension and a set of multiple date dimensions. Each date dimension is converted into a time series and a value quality factor is determined for each time series that represents a level of data quality for the time series. A time series informative factor is determined for each time series that represents how informative the time series is within a specified time window. An insight score is determined, for each time dimension, based on the determined value quality factors and the determined time series informative factors. The insight score for the time dimension is provided, for at least some of the time dimensions.