Personalized Data Visualization Engine Using Modular Aggregation
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Solution Overview
Problem
Existing data visualization systems face challenges in creating targeted advertisements without sufficient user data, requiring user authentication and data aggregation from various sources to personalize visual content effectively.
Innovation Solution
A data visualization engine that includes modules for user authentication, data aggregation, and visualization, allowing for the creation of personalized advertisements and virtual cards by combining user profile and transaction data with additional data sources, using dynamic visualizations such as images and videos to tailor content based on user demographics and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user authentication and data aggregation from multiple sources are implemented to create personalized visual content, then the personalization quality and targeting accuracy improve, but the system complexity and data processing requirements increase
Solution Approach 1:
The system divides the complex data processing task into separate modules: an authentication module that verifies user identity, a data aggregation module that collects information from multiple sources, and a visualization module that creates personalized content. This segmentation allows each module to specialize in one function, improving personalization quality while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediate data structures and processing layers that mediate between raw data from multiple sources and the final visualized output. These intermediaries organize and standardize data before visualization, enabling high-quality personalization without directly increasing the complexity of the core visualization engine.
2Loss of information
If dynamic visualizations are created by combining user profile and transaction data with additional data sources, then the relevance and engagement value improve, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data aggregation and user authentication before the visualization generation phase. By pre-processing and organizing data from multiple sources in advance, the system reduces the computational burden during actual visualization creation, thereby maintaining high data relevance while reducing processing time when visual content is needed.
3Measurement precision
If multiple data sources are aggregated to enhance user profiling accuracy, then the targeting precision improves, but the resource utilization and processing overhead increase
Solution Approach 1:
The patent applies local quality by selectively aggregating data from different sources based on specific user context and visualization requirements. Rather than processing all available data uniformly, the system identifies and processes only the relevant data subsets needed for each specific personalization task, improving profiling accuracy while reducing overall processing overhead and resource consumption.
Data Source
AI summary
Embodiments of computer implemented methods and systems for visualization of data are described. One example embodiment includes receiving authentication data related to a user, establishing an identity of the user based on the user authentication data, and receiving profile data associated with the user in response to the establishing of the identity of the user. The example embodiment may further include receiving transaction data associated with the user, selectively aggregating the profile data with the transaction data as aggregated user data, visualizing the aggregated user data as a data visualization, the data visualization being a composition of visual media corresponding to the aggregated data, and providing an address to the data visualization such that the data visualization may be referred to in other applications.