Information Retrieval System for Interactive Database Queries
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Solution Overview
Problem
Existing business intelligence (BI) systems require complex processes and significant time to translate raw data into meaningful information, often involving multiple IT specialists and failing to provide interactive user experiences when processing large volumes of data.
Innovation Solution
An information retrieval system that converts unstructured ad-hoc search queries into structured query instructions for relational databases, utilizing a distributed in-memory database with a novel query execution engine to enable fast data retrieval and interactive response times, allowing business users to search using simple terms and receiving relevant data suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional BI systems are used to translate raw data into meaningful information, then data processing capability is provided, but the process is complex and time-consuming requiring multiple IT specialists
Solution Approach 1:
The patent introduces an automated intermediary system comprising a natural language processing module and a query generation module that acts as a mediator between business users and the relational database. This intermediary automatically translates user-friendly search queries into optimized SQL queries, eliminating the need for IT specialists while maintaining high processing speed and reducing system complexity from the user perspective.
2Loss of time
If traditional BI systems process large volumes of data, then comprehensive data analysis is achieved, but interactive response times are not met
Solution Approach 1:
The patent changes the execution parameters by implementing a hybrid query processing approach that combines in-memory computing for aggregate functions with traditional disk-based storage for large volume data. The system dynamically adjusts processing parameters based on query types, using vectorized operations and parallel processing to achieve both fast response times and high data processing capacity simultaneously.
3Ease of operation
If Internet search engines are used for searching, then simple string matching is provided, but intelligence to search structured data and correlate results is lacking
Solution Approach 1:
The patent segments the search process into distinct functional modules: a natural language processing module that interprets user intent, a query generation module that translates intent into structured SQL queries, and a result processing module that correlates and aggregates data based on relational database schemas. This segmentation enables both simple user interaction and intelligent processing of structured data relationships.
Data Source
AI summary
An information retrieval system converts unstructured ad-hoc search queries into structured search instructions that retrieve data in a structured relational database or an unstructured database. Data from the database is uploaded into a distributed in-memory database system. Tokens are automatically generated based on attributes, measures, and other metadata extracted from the relational database. The tokens are then compared with the non-structured ad-hoc user search queries. The information retrieval system uses the tokens to identify or predict what structured data is associated with user search queries. The tokens guide the user through a set of search terms that the system then uses to generate the structured query instructions. The structured query instructions retrieve specific data and answers from in the database system.