Dynamic OLAP Cube Custom Grouping Without Rebuild
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data warehouse systems require time-consuming rebuilding or reprocessing of multidimensional database cubes whenever alterations are made, which can take hours or days, especially in large data volumes, making real-time modifications impractical.
Innovation Solution
A system and method for modifying multidimensional database cubes on the fly, allowing user-defined custom grouping of dimensions, adding new attributes or levels without rebuilding the underlying structure, using an interface component to gather user information and a modification component to alter the cube representation, enabling immediate availability of modified cubes for querying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional multidimensional database cube modification methods are used, then data integrity and structural consistency are maintained, but the modification time becomes excessively long (hours or days) due to complete rebuilding requirements
Solution Approach 1:
The patent segments the cube modification process into two distinct parts: (1) modification of the relational source tables which maintains data integrity, and (2) generation of a new cube representation from the modified tables. This segmentation allows the system to avoid complete cube rebuilding while ensuring structural consistency through controlled regeneration of only the affected cube portions.
Solution Approach 2:
The system performs preliminary modification of the relational source tables before cube regeneration. By pre-modifying the underlying relational structure and then generating the cube representation from these modified tables, the system avoids the time-consuming process of complete cube rebuilding while maintaining data integrity through the structured modification approach.
2Manufacturing precision
If complete cube rebuilding is performed to maintain structural consistency, then data accuracy is ensured, but productivity decreases due to the time-consuming nature of the process
Solution Approach 1:
The patent divides the cube modification into selective regeneration of affected cube portions rather than complete rebuilding. This segmentation maintains data accuracy for modified sections while improving productivity by avoiding unnecessary regeneration of unchanged cube portions.
Solution Approach 2:
The system applies local quality by regenerating only the specific cube portions that are affected by modifications rather than uniformly rebuilding the entire cube. This approach ensures data accuracy where needed while maintaining high productivity by leaving unaffected portions unchanged.
3Adaptability or versatility
If user-defined custom groupings are added to dimensions, then analytical flexibility and adaptability improve, but system complexity increases due to additional dimension attributes and levels
Solution Approach 1:
The patent implements dynamic dimension structures where custom groupings can be added or modified at runtime without requiring complete cube rebuilding. This dynamic approach allows users to flexibly adjust dimension attributes and levels based on analytical needs while the system automatically manages the complexity through on-demand regeneration of affected cube portions.
Solution Approach 2:
The system provides universal dimension modification capabilities that work across different cube types and scenarios. The same mechanism handles both simple attribute additions and complex custom grouping creations, reducing the perceived complexity for users while maintaining analytical flexibility.
4Speed
If real-time cube modification is implemented, then responsiveness to analytical needs improves, but the risk of structural inconsistencies and data errors increases
Solution Approach 1:
The system performs preliminary modification of relational source tables with built-in consistency checks before generating the cube representation. This preliminary action ensures structural consistency is maintained even during rapid modifications, as the relational structure serves as a validated foundation for cube generation.
Solution Approach 2:
The patent implements feedback mechanisms where the system validates modifications against structural constraints during the modification process. This feedback loop ensures that even real-time modifications maintain structural consistency by detecting and preventing invalid changes before they propagate through the cube structure.
Data Source
AI summary
Custom grouping for dimension members of an On-Line Analytical Processing (OLAP) cube is facilitated by an interface component that receives a grouping from a user. A modification component alters an attribute of a session OLAP data cube or a virtual OLAP multi-dimensional data cube so that the change becomes readily available without the time-consuming delay associated with rebuilding or reprocessing the underlying structure. A data scheme generation component produces a data definition language (DDL) definition of a user defined attribute executed by an execution component to affect the change to a data cube. Custom groupings can provide an intermediate dimension level for drill down and/or drill up or roll-up between a level with few members and a level with numerous members. A separate dimensional attribute or level can be created and a group defined comprising or mapping a set or collection of discrete members.