OLAP Cube Generation via Single Schema File
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Solution Overview
Problem
Existing systems for generating and managing online analytical processing (OLAP) cubes are complex and time-consuming, requiring multiple schema files and lacking efficient mechanisms for discovery and rapid query execution.
Innovation Solution
The system decouples schema from instance, allowing OLAP cubes to be generated and managed using a single configuration file, with automatic generation of data-source properties and column classifications, and implements caching, prefetching, and schema-less database approaches to reduce latency and improve query efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple schema files are used to define OLAP cubes, then data organization and query capability are improved, but system complexity and time to write queries increase
Solution Approach 1:
The patent combines multiple schema files into a single schema file that defines the OLAP cube structure. This single schema file contains all necessary definitions for dimensions, measures, and data sources, eliminating the need to manage multiple separate schema files and significantly reducing the time required to write and execute queries.
Solution Approach 2:
The single schema file serves multiple functions: it defines the cube structure, specifies data source connections, establishes dimension and measure relationships, and provides query execution instructions. This multi-functional approach replaces the need for multiple specialized schema files while maintaining full query capability.
2Speed
If OLAP cubes are pre-generated and cached, then query execution speed is improved, but system memory usage and initial processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and caching aggregate results for commonly queried dimensions and measures. These pre-computed results are stored in memory and reused for subsequent queries, significantly speeding up query execution without requiring full cube regeneration each time.
Solution Approach 2:
The system dynamically adjusts caching parameters based on query patterns and data characteristics. It selectively caches results for specific dimension combinations and measure aggregations that are most frequently accessed, optimizing the balance between memory usage and query performance rather than caching everything uniformly.
3Reliability
If comprehensive cube definitions are created upfront, then query accuracy and data consistency are improved, but development time and flexibility to adapt to new requirements decrease
Solution Approach 1:
The OLAP cube system is designed to be dynamic rather than static. The single schema file can be easily modified to add new dimensions, measures, or data sources without requiring complete redefinition. The system automatically adapts to schema changes and updates the cube structure accordingly, maintaining data consistency while enabling rapid adaptation to new analytical requirements.
Solution Approach 2:
The cube definition is segmented into modular components (dimensions, measures, data sources) within the single schema file. Each component can be independently defined, modified, or extended without affecting the entire cube structure. This modular approach maintains data consistency through structured relationships while providing flexibility to add or modify specific elements as needed.
Data Source
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AI summary
Systems, methods, and storage media for generating an online analytical processing cube (OLAP) are disclosed. Exemplary implementations may: receive a cube definition file; access a data-source; generate a data-source property configuration for the data-source using the cube definition file to; determine each of respective parsed data from the data-source is a key, attribute, or measure; and generate the OLAP cube by combining the cube definition file and the data-source property configuration for the determined parsed data from the data-source.