Dimensional Hierarchy Development with AI Guidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The development of dimensional hierarchies in planning applications is cumbersome, leading to data entry errors and undetected errors that limit the performance of planning applications, as existing systems lack efficient tools for analysts to manage and update these hierarchies effectively.
Innovation Solution
A method and system that includes a user interface for examining change request data, generating change request records, and presenting prompting data to guide analysts in modifying dimensional hierarchies, utilizing data sources like ODBC, ODBO, XML, and JSON databases, and implementing machine learning for predictive analytics to automate updates and improve data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If analysts manually develop and maintain dimensional hierarchies in planning applications, then the hierarchies can be customized to specific needs, but the process becomes cumbersome and error-prone
Solution Approach 1:
The system enables self-service through automated error detection and correction mechanisms. The AI model automatically analyzes dimensional hierarchy data, identifies potential errors based on learned patterns from historical data, and suggests corrections without requiring manual intervention from analysts, thus improving accuracy while reducing operational burden
Solution Approach 2:
The system implements feedback loops where the AI model continuously learns from historical dimensional hierarchy data and user corrections. The model provides feedback to analysts about potential errors and validates changes, creating an iterative improvement process that enhances both accuracy and ease of maintenance over time
2Reliability
If traditional manual methods are used to update dimensional hierarchies, then analysts have full control over changes, but errors remain undetected and limit application performance
Solution Approach 1:
The AI model serves as an intermediary between the analyst and the dimensional hierarchy data. It automatically detects potential errors in proposed changes, validates data consistency, and provides guidance before changes are committed, thereby improving reliability without requiring complex manual verification processes
Solution Approach 2:
The system performs preliminary error detection and validation before changes are actually committed to the dimensional hierarchy. The AI model analyzes proposed changes in advance, identifies potential issues, and prevents erroneous changes from being applied, thus improving reliability while maintaining system simplicity
3Productivity
If no automated guidance is provided to analysts, then the system remains simple, but data entry errors increase and maintenance becomes inefficient
Solution Approach 1:
The system replaces manual mechanical processes of error checking and validation with an AI-based intelligent system. The machine learning model automatically analyzes dimensional hierarchy data, identifies patterns indicative of errors, and provides guided recommendations, significantly improving productivity while the complexity is managed through automated processes rather than manual procedures
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining change request data defined by the certain analyst user with use of a user interface of a planning application, the change request data defining a change to the dimensional hierarchy used by the planning application, wherein the user interface includes an area that displays a dimensional hierarchy being authored by a certain analyst user; generating a change request data record in dependence on the examining, wherein the change request data record specifies attributes of the change to the dimensional hierarchy; and using data of the change request data record to present prompting data for guiding the certain analyst user in further changing the dimensional hierarchy.