Object-Based Natural Language Querying for Precise Data Analysis
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Solution Overview
Problem
Modern data centers face challenges in efficiently processing large volumes of machine-generated data and providing timely, intelligent responses to user queries due to the lack of detailed knowledge and experience required for data analysis, leading to inefficient and reactionary data science teams.
Innovation Solution
A data analysis system that allows users to submit free-form natural language queries, utilizing a data object model and machine learning to identify relevant artifacts and provide responses without the need for detailed knowledge of query languages, thereby reducing resource utilization and response time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis systems require detailed knowledge of query languages and complex data structures, then data analysis precision and reliability are improved, but user accessibility and ease of operation deteriorate
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language queries into formal data analysis operations. This mediator enables users without technical expertise to perform complex data analysis by simply stating questions in plain language, while the system handles the complex query translation and data retrieval processes automatically.
Solution Approach 2:
The patent replaces the mechanical requirement for users to manually construct complex queries using specific syntax and data structure knowledge with an automated natural language understanding system. The machine learning-based NLP component substitutes for the manual query building process, maintaining analysis precision while eliminating the need for users to learn complex query languages.
2Reliability
If data science teams manually process large volumes of machine-generated data, then analysis depth and reliability are improved, but processing time and resource consumption increase
Solution Approach 1:
The patent implements a system where the data analysis platform performs self-service processing of large datasets through automated machine learning algorithms and natural language processing. Instead of requiring manual intervention from data science teams for each query, the system autonomously executes complex analysis operations, retrieves relevant data, and generates responses independently, significantly reducing processing time while maintaining reliability through automated validation processes.
Solution Approach 2:
The patent employs preliminary data indexing, categorization, and pre-processing operations that prepare large volumes of machine-generated data in advance. By organizing and pre-processing data structures before queries arrive, the system enables rapid retrieval and analysis when users submit questions, reducing the time required for each individual analysis operation while maintaining comprehensive search capabilities.
3Loss of information
If data centers store and process large volumes of machine-generated data, then data completeness and information availability are improved, but storage requirements and processing complexity increase
Solution Approach 1:
The patent extracts and isolates relevant information from large volumes of machine-generated data through automated natural language processing and machine learning algorithms. By identifying and extracting only the pertinent data points that answer user queries, the system reduces the complexity of processing entire datasets while maintaining complete information availability. The extraction process filters and selects relevant data, making the processing of large data volumes more manageable and efficient.
Data Source
AI summary
A data analysis system presents a user interface to allow a user to provide a natural language query pertaining to a dataset, wherein the dataset is associated with a data object model comprising a plurality of objects and receives, via the user interface, user input specifying the natural language query. The data analysis system further modifies, in the user interface, the user input to visually indicate one or more portions of the natural language query that each represent one of the plurality of objects and presents, in the user interface, a response to the natural language query, the response being based on data from the dataset, the data corresponding to the one of the plurality of objects.