Graph Embeddings for Tabular Data Change Point Prediction
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Solution Overview
Problem
Conventional systems fail to accurately predict change points in tabular data due to their inability to leverage relationships across rows, relying on independent analysis of each data entry, which limits accuracy and requires specialized knowledge, making it difficult to identify errors and improve models.
Innovation Solution
A graph-based approach that converts tabular data into time-stamped graphs, generates graph embeddings, and processes them using a machine learning model to predict change points on a node level basis, capturing relationships across rows and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems analyze each data entry independently, then the process is simple and straightforward, but prediction accuracy deteriorates due to inability to leverage relationships across rows
Solution Approach 1:
The patent transforms the analysis from a one-dimensional row-by-row independent analysis to a multi-dimensional graph-based analysis that captures relationships across rows. By representing data entries as nodes and their relationships as edges in a graph structure, the system enables prediction accuracy to improve through leveraging cross-row relationships while managing complexity through structured graph representations and embedding techniques.
2Ease of operation
If specialized knowledge is required to design and implement artificial intelligence models, then model performance can be optimized, but accessibility and ease of operation deteriorate due to limited availability of skilled personnel
Solution Approach 1:
The patent introduces graph embeddings as an intermediary representation that bridges the gap between complex graph structures and machine learning models. This intermediary layer automatically captures relationship patterns without requiring manual feature engineering or specialized knowledge, making the system easier to implement while maintaining reliability through the mathematical properties of embedding transformations.
3Measurement precision
If the process for obtaining high-quality data is complex and time-consuming, then data quality improves, but productivity deteriorates due to extended data preparation time
Solution Approach 1:
The patent changes the fundamental parameter of data representation from traditional tabular format to graph-based structure with embeddings. This parameter transformation automatically extracts meaningful features and relationships during the embedding generation process, improving data quality for prediction tasks while reducing the manual data preparation time and increasing processing efficiency through automated feature extraction.
Data Source
AI summary
Systems and methods for predicting change points in tabular data. In some aspects, the systems and methods provide for generating time-stamped graphs based on data entries and corresponding time stamps. Each graph of the time-stamped graphs corresponds to a data entry and is representative of one or more events associated with a time stamp corresponding to the data entry. The graph is independent of any events before or after the time stamp. For each graph of the time-stamped graphs, a set of graph embeddings is generated based on the graph and processed using a machine learning model to predict an occurrence of a change point in the data entries.