Integrated Weather GUI for Exchange-Time Market Data Alignment
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Solution Overview
Problem
Conventional digital distribution platforms face challenges in managing and integrating disparate data types, particularly weather and market data, for real-time distribution, leading to transmission delays, data handling errors, and difficulty in aligning and integrating these data types in an intelligent manner without significant computational burden or system complexity.
Innovation Solution
A system and method for integrating weather and market data using an interactive graphical user interface (GUI) that collects, stores, and generates symbology instructions to create an integrated presentation package, updating concurrently with data changes, and leveraging a time series server to align data in 'exchange time' for seamless integration and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time data distribution is implemented with increasing data volume and volatility, then data freshness and user interaction capability are improved, but transmission delays and data handling errors increase
Solution Approach 1:
The patent implements a pre-fetching mechanism that anticipates user data requests and retrieves data before it is actually requested. The system analyzes user behavior patterns and proactively loads data into the distribution buffer, ensuring that when users request data, it is already available or nearly available, thereby minimizing transmission delays while maintaining data freshness.
Solution Approach 2:
The patent introduces a distributed caching layer as an intermediary between data sources and end users. This caching layer stores frequently accessed data locally at edge nodes, reducing the need for repeated transmissions from central servers. The cache acts as a buffer that delivers data faster while maintaining consistency with the source, thus reducing transmission delays without sacrificing data freshness.
2Reliability
If real-time data distribution is implemented with increasing data volume and volatility, then data freshness and user interaction capability are improved, but data handling delays increase
Solution Approach 1:
The patent segments the data distribution system into modular components: data ingestion modules, processing modules, caching modules, and delivery modules. Each module handles specific data transformation tasks independently and in parallel. This segmentation allows the system to process different types of data through optimized pipelines simultaneously, reducing overall data handling delays while maintaining real-time freshness.
Solution Approach 2:
The patent dynamically adjusts data processing parameters such as batch sizes, compression levels, and filtering thresholds based on real-time system load and data characteristics. When system resources are abundant, the system processes data with higher fidelity and less aggregation. When resources are constrained, it automatically adjusts parameters to maintain throughput, thereby reducing data handling delays while preserving essential data freshness.
3Loss of information
If disparate data types are integrated in an intelligent manner, then meaningful information and data alignment are improved, but computational burden and system complexity increase
Solution Approach 1:
The patent implements a universal data schema framework that can accommodate multiple disparate data types through a common structure. The framework uses standardized data models and normalization techniques that allow different data sources (weather data, market data, sensor data) to be integrated into a unified representation. This universal approach enables intelligent integration and meaningful correlations between different data types without requiring separate complex processing systems for each data type.
Solution Approach 2:
The patent employs automated data integration algorithms that self-configure and adapt to new data types without requiring manual system reconfiguration. The system automatically discovers data relationships, aligns data schemas, and integrates new data sources through machine learning-based pattern recognition. This self-service capability reduces system complexity by eliminating the need for manual integration efforts while maintaining high-quality intelligent integration of disparate data types.
Data Source
AI summary
Data integration and distribution systems. A system includes a graphical user interface (GUI). Weather and market data are collected. A weather symbology including symbol elements linked to segments of the collected weather data and rules for generating weather symbology instructions are stored. The GUI is generated for display on a user device. A weather symbology instruction is determined based on at least one requested symbol element indicated in a weather data request and the rules. A weather forecast dataset is created from among the collected weather data based on the weather symbology instruction. A presentation package including the weather forecast dataset and the collected market data is generated such that the weather forecast dataset is integrated with the collected market data. The presentation package is presented on the GUI and updated concurrent with changes to at least one of the weather data, the market data and user input.