LLM Data Enrichment for Missing Root Cause Insights
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Solution Overview
Problem
Existing data analytics systems struggle to provide comprehensive insights by suggesting root causes of data points that are not explicitly represented in the user's dataset.
Innovation Solution
Utilizing a high-powered Large Language Model (LLM) to analyze datasets and suggest data-related factors that are not explicitly present, enhancing data enrichment by scraping relevant data from external sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data analytics systems are used, then the system complexity remains low, but the depth and comprehensiveness of analytical insights is insufficient
Solution Approach 1:
The patent introduces an LLM-based intermediary component that acts as a mediator between the user's dataset and external web sources. This intermediary automatically generates search queries based on the dataset, scrapes relevant external data, and synthesizes enriched insights, thereby reducing information loss without requiring the user to directly manage the complex processes of data collection and integration
Solution Approach 2:
The system segments the data enrichment process into distinct modular components: (1) LLM-based query generation from dataset metadata, (2) web scraping module for external data collection, (3) data synthesis and enrichment module. This segmentation allows each component to specialize in specific tasks, improving overall insight quality while making the complex system more manageable and maintainable
2Loss of information
If data enrichment from external sources is performed, then the breadth of analytical information increases, but the time required for data processing increases
Solution Approach 1:
The system performs preliminary actions by generating search queries from dataset metadata before actual data scraping begins. The LLM analyzes the dataset structure and generates optimized search queries in advance, which then guide the web scraping process. This preliminary query generation step reduces the time needed for iterative searching and improves the efficiency of external data collection
Solution Approach 2:
The patent replaces manual mechanical processes of data research and collection with an automated LLM-based system. Instead of manually searching external sources and compiling data, the system uses LLMs to automatically generate queries, scrape relevant information from multiple web sources, and synthesize enriched insights, dramatically reducing the time required for comprehensive data enrichment
Data Source
AI summary
Embodiments described herein are generally related to data analytics environments, and are particularly directed to systems and methods for use with a data analytics environment to provide enrichment of data from external sources via a large language model. In accordance with an embodiment, the systems and methods can utilize a high-powered LLM to suggest data-related factors that may not be explicitly represented in the user's dataset. For example, upon a user's selection of a pair of datapoints, the systems and methods can utilize a LLM to provide suggestions for root causes of those datapoints, even though such root causes are not explicitly represented in the dataset.