Predictive Database Analysis Under Low-Latency Constraints

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Solution Overview

Problem

Existing database analytic tools are inefficient, costly, and require substantial configuration and training, limiting accessibility and utility of large volumes of data stored in complex database systems.

Innovation Solution

Implementing low-latency predictive database analysis that automatically identifies and prioritizes data based on probabilistic utility, generating autonomous-analysis data beyond user requests, and optimizing resource utilization based on latency constraints.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If existing database analytic tools are used, then data analysis capability is provided, but efficiency is poor and resource consumption is high

Engineering Contradiction:

Improvedatabase analysis efficiencyVSAvoidresource consumption

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system performs preliminary actions by predicting and preparing data analysis tasks before they are explicitly requested. The predictive module anticipates potential analysis needs and pre-processes relevant data, allowing the system to respond with pre-computed results rather than performing full analyses from scratch, thereby improving efficiency and reducing resource consumption.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The database analysis system performs self-service through autonomous predictive analysis. The system independently identifies valuable data insights and executes analysis tasks without requiring explicit user commands for each analysis, using contextual understanding and machine learning to autonomously determine what analysis is needed and perform it efficiently.

Inventive Principle:

Principle #25Self-service

2Ease of operation

If existing database analytic tools are used, then data analysis is performed, but substantial configuration and training is required

Engineering Contradiction:

Improvesystem configuration and training requirementVSAvoidconfiguration and training complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system eliminates the need for extensive configuration and training by performing self-service through autonomous predictive analysis. The machine learning models automatically adapt to data patterns and user contexts without requiring manual configuration, making the system immediately usable while maintaining high analytical capability.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system changes the operational parameters from manual configuration-based to automated prediction-based. Instead of requiring users to configure analysis parameters, the system uses machine learning models that automatically adjust parameters based on predicted needs and contextual data, significantly reducing the complexity barrier for users.

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If comprehensive data analysis is performed, then more insights are obtained, but latency increases

Engineering Contradiction:

Improvedata insights coverageVSAvoidanalysis latency

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system applies partial action by performing only the necessary analysis depth required for each specific query context. The predictive module determines the appropriate level of analysis depth based on the query characteristics and user needs, avoiding unnecessary comprehensive analyses that would increase latency while still providing sufficient insights.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system performs preliminary predictive analysis to identify the most relevant data and analysis approaches before executing the full query. This pre-computation of analysis priorities allows the system to efficiently allocate resources and reduce latency by avoiding unnecessary data processing steps while maintaining comprehensive insight coverage.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12554740B2Low-latency predictive database analysis

Publication Date: 2026.02.17 THOUGHTSPOT INC

AI summary

Low-latency autonomous-analysis includes obtaining data expressing a usage intent with respect to a low-latency database analysis system that intent omits data corresponding to user input expressly requesting low-latency autonomous-analysis, obtaining requested results data based on the data expressing the usage intent, outputting requested visualization data representing at least a portion of the requested results data for presentation to a user, and, in response to outputting the requested visualization data, obtaining low-latency autonomous-analysis data by performing low-latency autonomous-analysis based on the data expressing the usage intent by identifying an autonomous-analysis predicate based on the requested visualization data, obtaining a defined autonomous-analysis latency constraint, obtaining the low-latency autonomous-analysis data based on the autonomous-analysis predicate in accordance with the defined autonomous-analysis latency constraint, such that the low-latency autonomous-analysis data differs from the requested results data, and outputting at least a portion of the low-latency autonomous-analysis data for presentation to a user.