Data Aggregator GUI for Dynamic Disparate Dataset Queries
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Solution Overview
Problem
Conventional data aggregation platforms face challenges in managing and presenting disparate datasets that do not naturally fit into traditional time series formats, leading to difficulties in real-time data distribution, computational burden, and incomplete data presentation, especially with rapidly changing and large volumes of data.
Innovation Solution
The system aggregates disparate datasets into a singular datapoint using a synthetic aggregation wizard, enabling customizable data queries and interactive graphical user interfaces for real-time updates and user-defined mappings, allowing users to create aliases and subscribe to datasets.
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 naturally fit into time series formats cannot be easily presented or managed
Solution Approach 1:
The platform implements a universal data aggregation workflow that can handle multiple data types including time series, non-time series, and hybrid datasets through a single standardized interface. The system provides unified functions for data ingestion, aggregation, and presentation that work across different data formats without requiring separate specialized systems.
Solution Approach 2:
The system dynamically adjusts data processing parameters based on the input data type. When receiving non-time series data, the system modifies aggregation parameters and presentation formats accordingly, allowing the same platform to handle both traditional time series data and alternative data types with different structural characteristics.
2Speed
If the platform distributes large volumes of rapidly changing data in real-time, then data freshness is improved, but transmission delays and data handling delays introduce significant errors
Solution Approach 1:
The system performs preliminary data validation, normalization, and filtering at the data ingestion stage before distribution. By preprocessing data in advance and establishing quality thresholds, the system reduces the need for corrective handling during real-time distribution, thereby maintaining both speed and reliability.
Solution Approach 2:
The platform implements continuous data streaming with persistent connections between data sources and consumers. This eliminates repeated connection establishment and maintains uninterrupted data flow, ensuring that real-time distribution occurs without interruption while preserving data integrity through continuous monitoring.
3Loss of information
If conventional platforms require identifiers to be defined with associated history for each dataset, then data provenance is tracked, but the amount of key-value-pairs becomes effectively infinite and difficult to search, maintain, and support
Solution Approach 1:
The system extracts essential historical and provenance information from complete data histories, retaining only the critical attributes needed for tracking and identification. This selective extraction reduces the volume of stored key-value pairs while preserving the necessary information for data provenance and enabling efficient search and maintenance operations.
Solution Approach 2:
Instead of organizing data around exhaustive historical records with each identifier, the system inverts the approach by using compact identifiers that reference centralized, efficiently structured metadata repositories. This allows complete provenance information to be accessible without duplicating it across infinite key-value pairs.
4Device complexity
If the platform uses one-size-fits-all workflows with fixed filters, then system simplicity is maintained, but datasets with attributes that do not align with platform filters produce incomplete or irrelevant data presentation
Solution Approach 1:
The platform implements dynamic filter configuration that automatically adapts to the characteristics of each dataset. Filters are not fixed but are dynamically generated or selected based on the data type, structure, and attributes being processed, allowing the same simple workflow interface to handle diverse data types with appropriate filtering for each.
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.