Dynamic N-Dimensional Cubes Using Slice-Based Partitioning
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Solution Overview
Problem
Existing n-dimensional cube structures in data warehouse and OLAP systems are not well-suited for dynamic data environments, as they require full recalculation or recomputation when new dimensions or attributes are added, leading to inefficiencies and high computational burdens.
Innovation Solution
Implementing a slice-based partitioning scheme for n-dimensional cubes, where new dimensions or attributes can be added by creating new slices or modifying existing ones, with dependency links formed between slices and data points to enable partial precomputation, allowing for expansion or contraction without full recalculation, and using replication and load balancing to manage computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional n-dimensional cube structures are used, then data analysis capability is provided, but full recalculation is required when new dimensions are added, leading to high computational burden
Solution Approach 1:
The n-dimensional cube is segmented into multiple slices, where each slice represents a subset of the cube along a particular dimension. This segmentation allows individual slices to be updated independently when new dimensions are added, eliminating the need for full recalculation of the entire cube structure.
Solution Approach 2:
The cube structure is made dynamic by allowing slices to be added, removed, or modified on-demand based on analytical needs. New dimensions can be incorporated by creating new slices without restructuring the entire cube, enabling flexible adaptation to changing data requirements.
2Productivity
If traditional n-dimensional cube structures are used, then multidimensional data analysis is enabled, but updating the cube requires recomputing all data points at each intersection
Solution Approach 1:
Slices are precomputed and stored in advance, allowing query operations to retrieve preprocessed data without performing calculations at query time. When updates are needed, only the affected slices are recomputed rather than the entire cube, significantly reducing update time.
Solution Approach 2:
By dividing the cube into independent slices, the system can update only the specific slices that contain changed data, rather than recomputing all data points across the entire cube. This selective updating dramatically reduces both computation time and resource consumption.
3Adaptability or versatility
If new dimensions are added to traditional n-dimensional cubes, then analytical capability is enhanced, but the recomputation burden increases significantly
Solution Approach 1:
When new dimensions are added, the system creates new slices corresponding to the additional dimensional space without requiring recalculation of existing slices. This selective slice creation maintains computation efficiency while enhancing dimensional flexibility.
Solution Approach 2:
The slice-based structure provides a universal framework that handles both traditional and extended dimensional requirements through the same mechanism. New dimensions are accommodated by adding slices rather than restructuring existing data, maintaining efficiency across different analytical scenarios.
Data Source
AI summary
An online analytical processing system may comprise an n-dimensional cube structured using slice-based partitioning in which each slice comprises one or more hierarchies of data points. A region of a hierarchy may be classified according to computational demands associated with the region. A scaling or replication mechanism may be applied to the region based on the computational demands associated with that region.