Dynamic Queue Visualization for Data Processing Monitoring
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Solution Overview
Problem
In data processing environments, existing technologies lack effective methods for real-time monitoring and visualization of data processing applications and queues, making it difficult to understand relationships and status, especially when errors occur during job execution.
Innovation Solution
A computer-implemented method and system that dynamically analyzes information from data processing applications to identify queues and their relationships, generating a visual representation of data processing applications, queues, and their relationships, including status information, which can be displayed on a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If real-time monitoring and visualization of data processing applications and queues is implemented, then understanding of relationships and status is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent introduces a visualization module as an intermediary component that receives data from multiple data processing applications and queues, processes this information, and presents it in a unified visual format. This mediator approach allows real-time monitoring without requiring direct complex interactions between all monitoring components, thus reducing overall system complexity while maintaining visibility.
Solution Approach 2:
The visualization module serves multiple functions simultaneously: it monitors queue status, tracks data processing relationships, visualizes system architecture, and provides real-time status updates. By consolidating these monitoring functions into a single multi-functional component, the system avoids the complexity of multiple separate monitoring systems while achieving comprehensive visibility.
2Measurement precision
If dynamic analysis of information is performed during application execution to identify queues and relationships, then real-time visualization accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by establishing the visualization infrastructure and data collection mechanisms before full application execution begins. Queue relationships and initial status information are prepared and structured in advance, allowing the dynamic analysis during execution to focus only on updating existing knowledge rather than discovering everything from scratch, thus reducing real-time processing time.
Solution Approach 2:
The visualization module implements continuous feedback loops where information about queues and data processing relationships is dynamically analyzed and updated during application execution. This feedback mechanism allows the system to maintain high accuracy by continuously refining its understanding of system state while optimizing processing by only analyzing changes and updates rather than re-analyzing entire system states.
3Ease of operation
If visual representation of data processing environments is provided, then user understanding and error identification are improved, but information processing and generation overhead increase
Solution Approach 1:
The visualization module extracts only the essential and relevant information from the complex data processing environment - such as queue status, data flow relationships, and critical error states - and presents this extracted information in a simplified visual format. By selecting and displaying only the most important elements rather than attempting to visualize all system details, the system maintains user comprehension while reducing computational overhead for information processing.
Data Source
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AI summary
A method includes receiving information provided by a data processing application during execution of the data processing application. The information is indicative of at least one of a source of data for the data processing application and a destination of data from the data processing application. The method includes dynamically analyzing the information during execution of the data processing application to identify a queue in communication with the data processing application; and dynamically analyzing the information during execution of the data processing application to identify a relationship between the data processing application and the queue, including at least one of identifying that the queue is the source of data for the data processing application and identifying that the queue is the destination of data from the data processing application.