Context-Based Data Aggregation System for Dashboard Presentment
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Solution Overview
Problem
Current data aggregation and presentment technologies often overwhelm users with excessive information or provide insufficient details, failing to effectively summarize data insights from various sources, leading to inefficiencies in computing resources and manual input.
Innovation Solution
A context-based system that uses natural language processing algorithms for semantic parsing, identifies relevant network data sources, and generates personalized dashboards with predictive analytics, reducing unnecessary data and manual input through selective data retrieval and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If comprehensive data aggregation is performed to provide all information, then information completeness is improved, but user cognitive load and data overload increase
Solution Approach 1:
The system extracts only the most relevant and important data points from comprehensive datasets using natural language processing and semantic parsing. It identifies key entities, relationships, and insights while filtering out redundant information, thereby providing complete essential information without overwhelming the user with all available data.
Solution Approach 2:
The system applies different levels of data aggregation and summarization to different sections of the dashboard based on user needs and data importance. Critical information receives more detailed presentation while less important data is summarized, creating variable quality across different parts of the information display to optimize both completeness and usability.
2Measurement precision
If detailed data presentation is provided to ensure comprehensive information, then information accuracy is improved, but processing efficiency and resource consumption worsen
Solution Approach 1:
The system segments data processing into multiple stages: initial semantic parsing to identify key concepts, contextual analysis to determine relevance, selective retrieval of detailed information only for identified key elements, and hierarchical presentation. This segmentation allows accurate processing of only necessary data portions rather than entire datasets.
Solution Approach 2:
The system performs partial data retrieval and processing by focusing computational resources on extracting and presenting only the most relevant information needed to answer user queries or fulfill dashboard requirements, rather than processing and presenting all available data with equal detail.
3Loss of information
If manual data input and selection is required to customize data sources, then data relevance is improved, but user effort and time consumption increase
Solution Approach 1:
The system automatically performs semantic parsing of user interface elements, identifies relevant data sources and contexts, and configures data aggregation parameters without requiring manual user input. The system serves itself by autonomously determining what data to retrieve and how to present it based on contextual analysis of the user interface and user needs.
Solution Approach 2:
The system uses feedback from user interactions, interface context, and data analysis results to continuously refine and adjust data source selection and aggregation parameters. This automated feedback loop enables the system to learn user preferences and optimize data relevance over time without requiring explicit manual reconfiguration.
Data Source
AI summary
Systems, computer program products, and methods are described herein for context-based data aggregation and presentment. The present disclosure is configured to receive, from a user input device, a user input triggering data aggregation and presentment from a first user interface; initiate semantic parsing of information associated with the first user interface using natural language processing algorithms; capture contextual information associated with the first user interface based on at least the semantic parsing; determine one or more sources of network data based on at least the contextual information; retrieve information from the one or more sources of network data; and display, via a second user interface, the information from the one or more sources of network data on the user input device.