Graph Model Continuous Query for Real-Time Data Visualization
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Solution Overview
Problem
Modern computing systems generate vast amounts of data, making real-time data processing and visualization time-intensive, especially when visualization formats need to be modified, as existing methods are inefficient in handling large datasets and dynamic changes.
Innovation Solution
A distributed computing environment with a data exploration system that includes client devices, a data exploration system, control servers, and databases, enabling graph model creation, modification, and continuous query management, allowing for real-time data processing and visualization through a client application with graph editing, rendering, and query management modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time data processing is implemented, then data accessibility and responsiveness are improved, but processing time and computational resources increase
Solution Approach 1:
The system pre-generates visualizations from large datasets and stores them in a database before user requests. When a user requests data visualization, the system retrieves pre-computed results instead of processing the entire dataset in real-time, thus achieving fast response without actual real-time processing overhead.
Solution Approach 2:
The system allows users to select specific subsets of data fields and visualization parameters rather than processing the entire dataset. By focusing computation and rendering only on the selected local portion of the data, the system reduces processing time while maintaining relevance to user needs.
2Adaptability or versatility
If visualization format modifications are enabled, then adaptability and user flexibility are improved, but processing time and computational overhead increase
Solution Approach 1:
The system pre-computes and stores multiple visualization formats and aggregations of the dataset in advance. When users request different visualization formats, the system retrieves pre-prepared variants from the database rather than generating them on-demand, enabling format flexibility without real-time processing delays.
Solution Approach 2:
The system creates and stores multiple copies of the data in different visualization formats and aggregation levels. These pre-generated copies can be quickly retrieved and displayed without reprocessing the original large dataset, thus enabling format modifications with minimal time penalty.
3Adaptability or versatility
If integrated data exploration and modeling is implemented, then system functionality and user capability are improved, but device complexity and system architecture increase
Solution Approach 1:
The system combines data exploration, data modeling, and visualization generation into a single integrated platform. By merging these functions into one unified system with a common database and interface, the architecture manages complexity through consolidation rather than separate independent systems.
Solution Approach 2:
The system introduces an intermediary layer (the data exploration system with graph model database) between the raw data sources and the user interface. This intermediary handles complex processing, storage, and transformation tasks, simplifying the overall architecture by centralizing complexity in a dedicated intermediate component.
Data Source
AI summary
Techniques are provided for data exploration. An application is provided for execution on a client computer to display a plurality of data fields associated with one or more data sources, receive user input specifying a set of one or more graph parameters based on one or more selected data fields, generate a graph model, and transmit a continuous query request corresponding to the graph model. In response to receiving the continuous query request from the application, a continuous query is generated and stored in association with the graph model. The continuous query is periodically executed to generate a plurality of computed result sets over time by accessing the one or more data sources. A selected computed result set is provided to the application. The application is configured to render a graph in the graph rendering interface based on the selected computed result set.