Dynamic Weighting Schema for Unified Dissimilar Data View
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Solution Overview
Problem
The challenge lies in providing a unified view of dissimilar data, where determining the specific facts, statistics, or items to be monitored, stored, and tracked, as well as representing the relative importance or weighting of each set of data in a collective representation, becomes complex, especially when adjusting for 'what if' scenarios.
Innovation Solution
A computer-implemented method and system that collect data attributes with associated evaluation criteria, allowing for dynamic adjustment and application of these criteria to collectively represent and modify the data, enabling flexible monitoring and visualization of transaction data through a mechanism that supports instrumentation of software assets at various granularities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple providers and sources is collected to form a unified view, then the comprehensiveness of the data representation is improved, but the complexity of determining relative importance and weighting increases
Solution Approach 1:
The patent introduces an intermediary weighting determination mechanism that mediates between multiple data sources and the unified view. This intermediary layer automatically determines relative importance weights for different data attributes, reducing the complexity of manual weighting while maintaining comprehensive data representation. The intermediary translates diverse data sources into a standardized weighted format.
Solution Approach 2:
The system dynamically adjusts weighting parameters based on data characteristics, source reliability, and user preferences. By changing the parameters of data evaluation (weights, priorities, thresholds), the system can adapt to different scenarios without increasing structural complexity. This allows flexible control over how multiple data sources are integrated.
2Adaptability or versatility
If dynamic adjustment mechanisms are provided for evaluation criteria, then the adaptability of the system is improved, but the complexity of the monitoring system increases
Solution Approach 1:
The patent implements dynamic evaluation criteria that can be adjusted in real-time based on changing conditions, user needs, or data characteristics. The system transitions from static to dynamic weighting, allowing automatic adaptation without requiring complex manual reconfiguration. This dynamic approach maintains versatility while managing complexity through automated adjustment mechanisms.
Solution Approach 2:
The system incorporates feedback loops where the results of data evaluation are fed back into the weighting determination process. This feedback mechanism allows automatic refinement of evaluation criteria based on performance metrics, reducing the need for complex external control while improving adaptability. The feedback-driven adjustment simplifies the overall system architecture.
3Measurement precision
If granular instrumentation is applied to software assets, then the precision of monitoring is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The patent applies segmentation by breaking down software assets into instrumentable units with specific data attributes. Each segment can be monitored independently with precise metrics, yet the segmented data is automatically aggregated into a unified view. This segmentation approach maintains high monitoring precision while managing data collection complexity through modular organization.
Solution Approach 2:
The system employs universal data collection mechanisms that can handle multiple data types and sources through a single standardized interface. This multi-functional approach allows granular instrumentation across diverse software assets without requiring separate complex collection systems for each data type, thereby maintaining precision while reducing overall complexity.
Data Source
AI summary
Systems, methods and articles of manufacture allow adjusting the relative weighting associated with evaluation criteria associated with a unified view of dissimilar data. The operation generally includes collecting data regarding attributes of a user interacting with an application, where the collected data has associated evaluation criteria. The data is collectively represented according to the evaluation criteria. The systems, methods and articles of manufacture then allow dynamically modifying the evaluation criteria before evaluating and collectively representing the data according to the adjusted criteria.