Natural Language Querying Data Lake Contextual Knowledge Bases
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Solution Overview
Problem
Current data management systems struggle to efficiently organize, explore, and process increasing amounts of data, leading to bottlenecks in data-driven decision-making due to the need for specialized personnel to translate metadata into actionable insights for high-level decision-makers.
Innovation Solution
A system and method for querying data lakes using natural language, which involves parsing and identifying entities within a natural language query, mapping dependencies, constructing structured data queries, and generating visual outputs based on data type, format, and size, leveraging contextual knowledge bases to facilitate user-friendly data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If data analysts and information technology scientists are hired to manage and analyze metadata, then data organization and analysis capability is improved, but personnel costs and decision-making latency increase
Solution Approach 1:
The system enables self-service data querying by allowing users to directly input natural language questions and receive automated visual outputs without requiring specialized data analysts. The natural language processing system automatically parses queries, generates SQL code, executes queries, and creates visualizations, making the data analysis function self-serving rather than dependent on human intermediaries.
Solution Approach 2:
The patent introduces a natural language processing system as an intermediary between users and the data lake. This intermediary automatically translates natural language queries into structured SQL queries, executes them, and generates visual outputs, thereby eliminating the need for human data analysts as intermediaries while maintaining automated data retrieval and analysis capabilities.
2Loss of information
If data analysts translate information from managed data into insights, then data understanding is improved, but the ability of high-level decision-makers to ask relevant questions is limited
Solution Approach 1:
The system inverts the traditional data analysis workflow by allowing users to ask questions in natural language rather than requiring analysts to pre-define and present predefined insights. Instead of analysts translating data into insights for users, the system enables users to directly query the data lake using their own natural language, reversing the information flow and empowering end-users with direct data access.
3Measurement precision
If specialized personnel are used to process data, then data processing accuracy is improved, but device complexity and operational overhead increase
Solution Approach 1:
The patent replaces the mechanical system of human data analysts with an automated natural language processing system. The system uses NLP models to parse and understand natural language queries, automatically generates SQL code, executes queries against the data lake, and generates visual outputs, thereby substituting human cognitive processes with automated computational processes that maintain accuracy while reducing operational overhead.
Data Source
AI summary
A method of querying a data lake using natural language includes: receiving a natural language query directed to an electronic data lake; parsing the natural language query to determine a plurality of entities within the natural language query; identifying the plurality of entities using at least one contextual knowledge base, wherein the plurality of entities are compared against at least one entry in the at least one contextual knowledge base; mapping a dependency of the plurality of identified entities based on the parsed natural language query; constructing a structured data query based on the plurality of identified entities and the mapped dependency; and automatically generating a visual output of a result of the structured data query based on at least one characteristic from the set of: a data type, a data format, and a data size of the result of the structured data query.