Bayesian Network Data Fusion for High-Dimensional Insights
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Solution Overview
Problem
Fusing and visualizing diverse datasets from disparate sources with different granularities and lacking natural join keys poses challenges in deriving insights, especially when dealing with high-dimensional data, where traditional query mechanisms are inefficient and inaccurate.
Innovation Solution
The system learns a Bayesian network using a minimum spanning tree embedded in a graph derived from pair-wise mutual information values to predict a target attribute, enabling efficient data fusion and visualization through exact inference and conditional probabilistic tables, while also providing data completion and probabilistic joins across datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional query mechanisms are used to derive insights from high-dimensional fused data, then the data can be processed, but the efficiency and accuracy deteriorate
Solution Approach 1:
The patent introduces Bayesian networks as an intermediary mechanism between raw fused data and insight derivation. The Bayesian network learns probabilistic relationships from the data and enables efficient querying through conditional probability calculations, avoiding the inefficiency of traditional queries on high-dimensional data while maintaining accuracy through probabilistic inference
Solution Approach 2:
The patent transforms the data representation by learning a Bayesian network structure and parameters from the fused data. This parameter transformation converts raw high-dimensional data into a compact probabilistic model with learned conditional probability tables, enabling efficient and accurate queries through the model rather than direct data processing
2Quantity of substance
If datasets from disparate sources with different granularities are fused, then more information is available, but the complexity of data integration increases
Solution Approach 1:
The patent applies a universal Bayesian network framework that can handle diverse data types and granularities through a common probabilistic modeling approach. The Bayesian network structure automatically adapts to different data sources by learning appropriate relationships, providing a unified mechanism for integrating heterogeneous datasets without requiring source-specific processing logic
Solution Approach 2:
The Bayesian network learning process automatically discovers the data structure and relationships from the fused datasets without manual intervention. The system self-configures the network structure and conditional probability tables by learning from the data itself, eliminating the need for complex manual data integration configurations
Data Source
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AI summary
System and method for visual Bayesian data fusion are disclosed. In an example, a plurality of datasets associated with a topic are obtained from a data lake. Each of the plurality of datasets include information corresponding to various attributes of the topic. Further, the plurality of datasets are joined to obtain a joined dataset. Furthermore, distribution associated with a target attribute is predicted using Bayesian modeling by selecting a plurality of attributes (k) based on mutual information with the target attribute in the joined dataset, learning a minimum spanning tree based Bayesian structure using the selected attributes and the target attribute, learning conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure; and predicting the distribution associated with the target attribute by querying the conditional probabilistic tables, thereby facilitating visual Bayesian data fusion.