Data Aggregator GUI for Real-Time Querying of Disparate Datasets
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Solution Overview
Problem
Conventional data aggregation platforms struggle to manage and present disparate datasets that do not fit into traditional time series formats, leading to technical difficulties in real-time data distribution, incomplete data presentation, and increased computational burden, especially with rapidly changing and large volumes of data.
Innovation Solution
A system and method for aggregating disparate datasets into a singular datapoint using a synthetic aggregation wizard, enabling user-customizable data queries and aliases, with dynamic real-time updates via an interactive graphical user interface (GUI).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data aggregation platforms use traditional time series formats and workflows, then data presentation is standardized, but disparate datasets that do not fit traditional formats cannot be easily presented or queried
Solution Approach 1:
The patent introduces an intermediary layer (the system with processors and databases) that sits between the disparate data sources and the user interface. This intermediary automatically maps, normalizes, and aggregates data from various sources into a unified format, allowing diverse datasets to be presented without requiring changes to the underlying data structures or increasing system complexity.
Solution Approach 2:
The system provides a universal data aggregation platform that can handle multiple types of disparate datasets (commodity data, natural resource data, market data) through a single unified interface. The platform uses common data types and standardized workflows to manage diverse data sources, eliminating the need for separate systems for each data type.
2Speed
If data volume increases and data content changes rapidly, then real-time data distribution capability is improved, but transmission delays and data handling delays introduce significant errors
Solution Approach 1:
The system performs preliminary actions by pre-processing and normalizing data as it is received, creating a ready-to-distribute format in advance. This reduces the handling time required when data needs to be distributed, minimizing delays while maintaining accuracy. The system also pre-loads data into databases for quick retrieval and aggregation.
Solution Approach 2:
The patent replaces manual or mechanical data handling processes with automated electronic processing. The system uses computer processors and software algorithms to automatically aggregate, filter, and format data, eliminating the delays associated with manual data processing and reducing errors in data transformation.
3Loss of information
If conventional platforms implement traditional workflows with defined identifiers and history, then data traceability is maintained, but the amount of key-value-pairs becomes effectively infinite and difficult to search and maintain
Solution Approach 1:
The system extracts only the essential identifying characteristics from disparate datasets rather than retaining all possible key-value-pairs. By selecting and retaining only the most relevant data points for identification and tracing, the system maintains data traceability while significantly reducing the volume of data that needs to be searched and maintained.
Solution Approach 2:
The patent transforms the data structure by changing parameters from traditional detailed key-value-pairs to a more condensed format using common data types and standardized fields. This parameter change allows the system to maintain traceability through essential identifiers while making the data much more searchable and manageable.
4Device complexity
If one-size-fits-all conventional platforms use fixed filters for data retrieval, then system simplicity is maintained, but datasets with attributes that do not align with platform filters produce incomplete data presentation
Solution Approach 1:
The system implements dynamic filtering capabilities that adapt to the specific characteristics of each dataset. Rather than using fixed filters, the system automatically adjusts its filtering and aggregation logic based on the data being processed, allowing it to handle diverse data types (commodity, natural resource, market data) with a single unified workflow that remains simple to operate.
Data Source
AI summary
Systems and methods for generating a data aggregator interactive graphical user interface. An interactive graphical user interface (GUI) includes a selectable symbol region, a query region and a data results region. The selectable symbol region displays predefined symbols. The query region displays user input fields for generating queries. The system receives user input associated with the user input fields of the query region to form a filter set. The data results region is automatically updated responsive to the user input, to display one or more data values from among one or more databases associated with the filter set. The system receives a subscription request to save the filter set as a user-customized query. A custom symbol is created responsive to the subscription request that is associated with the filter set. The selectable symbol region is updated to display the custom symbol together with the predefined symbols.