Dynamic Data Extraction Using Key Influencing Factor Prioritization
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Solution Overview
Problem
Industrial environments face challenges in efficiently managing dynamic data processing due to changing data requirements and network conditions, leading to delays in data extraction and communication of critical information, which affects customer satisfaction and operational efficiency.
Innovation Solution
A system that identifies key influencing factors, preprocesses data, and dynamically adjusts data extraction processes using deep neural networks to prioritize and streamline data processing, ensuring timely availability of relevant data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data extraction processes are performed for all datasets without prioritization, then complete data coverage is achieved, but processing time and system resource consumption increase significantly
Solution Approach 1:
The patent segments the data extraction process by dividing datasets into prioritized groups based on key influencing factors. The system identifies and extracts data from high-priority datasets first, then progressively processes lower-priority datasets based on available resources and timing requirements, thereby reducing overall processing time while maintaining essential data completeness.
Solution Approach 2:
The patent performs preliminary identification and prioritization of key influencing factors before executing the full data extraction process. By pre-analyzing which factors are most critical and which datasets contain relevant information, the system can prepare extraction schedules and resource allocation in advance, significantly reducing actual processing time when extraction is needed.
2Loss of time
If data extraction frequency is increased to ensure timely data availability, then data freshness is improved, but system resource consumption and processing load increase
Solution Approach 1:
The patent implements dynamic data extraction scheduling that adjusts extraction frequency based on the priority and characteristics of each dataset. High-priority datasets associated with key influencing factors are extracted more frequently and with higher urgency, while lower-priority datasets are extracted less frequently or deferred, thereby optimizing the balance between data freshness and system resource utilization.
Solution Approach 2:
The patent changes extraction parameters such as frequency, batch size, and processing depth based on the identified key influencing factors. For critical datasets, the system increases extraction frequency and allocates more processing resources, while for less critical datasets, it reduces extraction frequency and uses minimal resources, thus maintaining productivity while ensuring timely availability of essential data.
3Ease of operation
If all datasets are processed with the same priority, then fairness in processing is maintained, but critical data may experience delays affecting operational efficiency
Solution Approach 1:
The patent applies local quality by assigning different processing priorities to different datasets based on their association with key influencing factors. Instead of uniform treatment, the system identifies specific datasets that contain information about critical factors (such as customer satisfaction drivers or operational bottlenecks) and gives them higher priority, ensuring that locally critical data is processed faster while maintaining overall system fairness through transparent priority assignment.
4Measurement precision
If data processing pipelines are customized for each dataset, then processing precision is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal data processing framework that can handle multiple datasets with different requirements through a single configurable pipeline. The system uses a common extraction engine that dynamically adjusts its behavior based on dataset priorities and key influencing factors, eliminating the need for separate customized pipelines for each dataset while maintaining processing precision through parameter-based configuration rather than structural duplication.
Data Source
AI summary
An embodiment provides for dynamic data extraction based on key influencing factors. The embodiment preprocesses data comprises key influencing factors from authentic sources. and extracts relevant features from the data. The embodiment represents key characteristics of the key influencing factors based on the relevant features and detects changes in the key influencing factors based on contextual shifts in the key influencing factor. The embodiment dynamically adjusts a data extraction process based in part on the contextual shift in the key influencing factor and identifying a top key influencing factor by analyzing data source. The embodiment classifying and ranking a first dataset from the data source based on the top key influencing factor and prioritizing a processing pipeline for the first dataset. The embodiment cloning the processing pipeline for a second dataset and provides an interface, via a consumption layer, for interacting with the first and second datasets.