Multidimensional Database Root Cause Analysis via Priority Queue
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Solution Overview
Problem
In multidimensional database environments, identifying the root cause of changes in query results across vast numbers of data dimensions is challenging, requiring users to sift through numerous dimensions to determine contributing factors, which is inefficient and time-consuming.
Innovation Solution
A system that automatically detects changes in query results and provides a customizable set of top contributing data dimensions using a priority queue mechanism, ranking changes based on their impact, to facilitate root cause analysis and generate key metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually analyze vast numbers of data dimensions to identify root causes of query result changes, then comprehensive analysis coverage is achieved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system performs automatic root cause analysis by having the multidimensional database server autonomously detect query result changes, analyze contributing data dimensions, and generate analysis reports without requiring manual user intervention. The server uses built-in logging capabilities and automated algorithms to identify root causes, transforming a manual analytical task into an automated self-service process that reduces time consumption while maintaining analysis accuracy
Solution Approach 2:
The patent replaces the manual mechanical process of user-driven dimension-by-dimension analysis with an automated computational system. The multidimensional database server uses automated logging, change detection algorithms, and contribution analysis to substitute human analytical efforts, thereby eliminating time-consuming manual operations while preserving comprehensive root cause identification
2Reliability
If users examine all data dimensions to ensure complete root cause analysis, then analysis thoroughness is improved, but operational complexity and difficulty increase
Solution Approach 1:
The system extracts and isolates only the most significant contributing data dimensions from the vast multidimensional dataset. Instead of requiring users to examine all dimensions, the automated analysis identifies and extracts the key dimensions that most significantly contributed to query result changes, presenting a focused subset that maintains analysis completeness while simplifying user interaction
Solution Approach 2:
The patent introduces an automated analysis intermediary layer between the raw multidimensional data and the user. This intermediary automatically processes the complex dimension analysis, filters out insignificant variations, and presents streamlined results. The intermediary handles the operational complexity internally while providing simplified outputs to users, thereby maintaining thoroughness without increasing user-facing complexity
3Loss of information
If the system provides detailed information on all contributing factors, then information completeness is improved, but information overload and difficulty in identifying key metrics worsen
Solution Approach 1:
The system segments the contributing data dimensions into hierarchical groups based on their level of contribution to query result changes. By organizing information into segments such as major contributors, minor contributors, and contextual factors, the system preserves complete information while structuring it in a manageable hierarchy that helps users quickly identify key metrics without being overwhelmed by detailed data from all dimensions
4Productivity
If automatic analysis is implemented to reduce manual effort, then productivity is improved, but system complexity increases
Solution Approach 1:
The patent leverages the existing multidimensional database server's built-in logging and data processing capabilities to perform root cause analysis. By making the server multi-functional—combining its existing query processing, data storage, and logging abilities with automated root cause analysis—the system achieves high productivity without requiring separate dedicated analysis infrastructure. This universal approach increases productivity while minimizing additional system complexity
Data Source
AI summary
In accordance with an embodiment, described herein are systems and methods for automatic root cause analysis and generation of key metrics in a multidimensional database. A system can comprise a computer and a multidimensional database server executing on the computer, wherein the multidimensional database server supports at least one hierarchical structure of data dimensions. One or more one or more user logs are created, the one or must user logs representing a plurality of operations performed by a plurality of users of the multidimensional database server and accessing the at least one hierarchical structure of data dimensions. Based upon historical data of the at least one hierarchical structure of data dimensions, a change in a query result of a user is detected. Based upon the detection of a change, a set of data dimensions can be provided to the user that contains the data dimensions most contributing to the change.