Temporal Importance Estimation for Distributed Enterprise Data
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Solution Overview
Problem
In distributed enterprise networks, quantifying the temporal importance of data is challenging due to the vast amount of data generated from various sources, with dynamic factors affecting importance, making it difficult to prioritize effectively.
Innovation Solution
A computer-implemented method and system that estimates temporal importance by receiving data, identifying its type, loading a corresponding plugin, extracting features, mapping them to pre-defined classes, and calculating an Importance Measure matrix, which is updated based on stakeholder inputs and a time factor, using matrix completion techniques when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is prioritized based on multiple dynamic factors and stakeholder inputs, then measurement precision of data importance is improved, but device complexity increases due to multiple plugins and matrix calculations
Solution Approach 1:
The system segments the importance measurement process into distinct data type plugins, each handling specific data types with specialized feature extraction and importance calculation logic. This modular segmentation allows precise measurement for each data type while managing overall system complexity through independent, reusable components.
Solution Approach 2:
The system dynamically changes parameters by incorporating temporal factors and stakeholder-specific weightages into the importance measurement. The importance measure is not static but adapts based on time-dependent decay factors and varying stakeholder priorities, enabling precise measurement that reflects real-world dynamics without requiring complete system redesign.
2Measurement precision
If comprehensive feature extraction and stakeholder inputs are collected, then importance measurement precision is improved, but loss of time increases due to extensive processing requirements
Solution Approach 1:
The system performs preliminary actions by pre-defining feature extraction methods, class mappings, and stakeholder weightages before actual importance measurement. Data type plugins are pre-configured with their specific features and calculation methodologies, allowing rapid processing during execution without requiring comprehensive analysis from scratch each time.
Solution Approach 2:
The system implements periodic action through time-dependent importance decay factors that automatically adjust importance measures based on elapsed time. This periodic adjustment mechanism efficiently captures temporal dynamics without requiring continuous re-evaluation of all data, reducing processing time while maintaining measurement precision.
3Adaptability or versatility
If multiple data types with dynamic factors are processed, then adaptability of the system is improved, but device complexity increases due to need for multiple data type plugins
Solution Approach 1:
The system achieves universality through a common importance measurement framework that handles multiple data types through standardized plugins. Each data type plugin follows a uniform structure with consistent interfaces for feature extraction, class mapping, and importance calculation, allowing the system to process diverse data types adaptably while managing complexity through standardized multi-functional components.
Data Source
AI summary
The present disclosure provides systems and methods to estimate Importance Measure (IM) and temporal Importance Measure (IM) for any type of data in a distributed enterprise network by involving direct and indirect stakeholders of the data in the estimation process. Firstly data type of the received data is identified. Data type plugins including pre-defined classes, IM matrix and Temporal IM matrix are loaded for the identified data type. Extracted features from the data are appropriately mapped against pre-defined classes and then the IM is estimated. Temporal IM is estimated taking into account the current time and the rate of change.