OLAP Cube Sparsity Management via Dimension Utilization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing sparsity in OLAP cubes, resulting from unpopulated cells, leads to increased query times during multidimensional analytical queries, as the number of unpopulated cells affects the efficiency of data retrieval and analysis.
Innovation Solution
A method is implemented where the frequency of dimension utilization is determined, and dimensions with lower utilization are combined with the multidimensional data structure, allowing for the regeneration and refreshment of OLAP cubes to prioritize frequently used dimensions, thereby managing sparsity and optimizing query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If dimensions are combined with the multidimensional data structure to reduce sparsity, then query processing time is reduced, but the complexity of managing dimension utilization frequency increases
Solution Approach 1:
The system implements feedback by continuously monitoring dimension utilization frequency and using this information to dynamically adjust which dimensions are combined with the multidimensional data structure. The feedback loop tracks how often each dimension is accessed during queries and uses this data to make informed decisions about dimension combination, thereby reducing sparsity while maintaining manageable complexity through data-driven management.
Solution Approach 2:
The system changes the parameter of dimension combination status based on utilization frequency thresholds. When a dimension's utilization frequency exceeds a certain threshold, it is combined with the multidimensional data structure; when it falls below the threshold, it is excluded. This dynamic parameter adjustment allows the system to optimize query performance by reducing sparsity while maintaining control over management complexity through configurable thresholds.
2Loss of information
If the multidimensional data structure includes all dimensions, then data completeness is improved, but the number of unpopulated cells increases leading to increased query times
Solution Approach 1:
The system extracts only the frequently utilized dimensions from the complete set of dimensions and combines them with the multidimensional data structure. By taking out and including only those dimensions that are actively used in queries, the system maintains data completeness for relevant information while avoiding the inclusion of rarely used dimensions that would contribute to sparsity and increased query times.
Solution Approach 2:
The system applies local quality by differentiating between dimensions based on their utilization characteristics. Frequently used dimensions are combined with the data structure to ensure data completeness where it matters most, while less frequently used dimensions are excluded to reduce sparsity. This selective approach ensures that data completeness is maintained locally in the most critical areas without the penalty of including all dimensions.
Data Source
AI summary
Embodiments of the present invention relate to a method, program product, and system for managing the percentage of unpopulated cells in a multidimensional data structure during the servicing of multidimensional analytical queries. In an embodiment, a multidimensional data structure is stored in a memory location of a computing device, wherein the multidimensional data structure includes a dimension that is a subset of a plurality of dimensions included in a relational database. The computing device determines a frequency of utilization of a dimension to service a multidimensional query request. The computing device combines the dimension with the multidimensional data structure responsive to the frequency of utilization.