Database Operation Recommendation via Predictive Modeling

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Users, especially casual or less proficient ones, face difficulties in selecting appropriate database operations for data processing in database management systems due to lack of knowledge or efficiency in using available operations, which can be time-consuming to learn.

Innovation Solution

A data analysis server employs a machine learning predictive model to recommend database operations to users based on historical data from advanced users, utilizing a data profiling module for context data and a database operation history module to improve operation selection efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If users manually select database operations without assistance, then they have full control over operation selection, but less proficient users experience difficulty and time consumption in learning and selecting appropriate operations

Engineering Contradiction:

Improveease of selecting database operationsVSAvoidtime to learn database operations

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system provides automated database operation recommendations based on historical data from advanced users, allowing the system to serve itself by inferring appropriate operations without requiring manual selection by less proficient users

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses feedback from historical database operations performed by advanced users to train predictive models that generate recommendations for less proficient users, creating a continuous learning loop that improves operation suggestions over time

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If the system provides comprehensive database operation options, then users have more choices for data processing, but users unfamiliar with operations struggle to identify appropriate choices

Engineering Contradiction:

Improvenumber of database operation optionsVSAvoiddifficulty in identifying appropriate operations

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The system introduces an intermediary layer of automated recommendations that mediates between the comprehensive list of available operations and the user's need for guidance, filtering and prioritizing operations based on historical success patterns

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary analysis of historical data and context before presenting operation options, pre-computing recommendations based on similar past operations so that users receive tailored suggestions before they need to make a selection

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10324947B2Learning from historical logs and recommending database operations on a data-asset in an ETL tool

Publication Date: 2019.06.18 INFORMATICA CORP

AI summary

A data analysis server maintains database operation history data and context data for database operations performed on tables by a set of training users. The data analysis server builds predictive models for using the maintained data to recommend database operations and operands to a set of guided users. The data analysis server trains the predictive models by determining and weighting features derived from context data that are predictive of performing database operations to tables with similar context data. Using the predictive model, the data analysis server generates recommended database operations and operands based on context data received from a data analysis application of a guided user and sends the recommendations to the data analysis application for presentation to the guided user.