Data Domains in Multidimensional OLAP Data Cubes
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Solution Overview
Problem
As databases used in online analytical processing (OLAP) systems become larger and more complex, existing OLAP tools face inefficiencies in querying and managing multidimensional data, leading to decreased performance.
Innovation Solution
The use of data domains to define and organize multidimensional data within OLAP data cubes, allowing for strategic and efficient querying, aggregation, and caching, by specifying parameters such as measures, dimensions, and partitions, enabling efficient data separation and granularity management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional OLAP tools are used to query and manage multidimensional data, then basic analytical functions can be provided, but performance deteriorates as databases become larger and more complex
Solution Approach 1:
The patent segments multidimensional data into distinct data domains, each representing a specific business concept or subject area. This segmentation allows the OLAP system to process and query specific domains independently, improving query efficiency by avoiding full-scan operations across the entire data cube. The segmentation principle is applied by organizing data into hierarchical domains with parent-child relationships, enabling targeted analysis without processing unrelated data.
Solution Approach 2:
The patent introduces data domains as an additional organizational dimension beyond traditional OLAP dimensions (time, product, location). This domain dimension provides a new perspective for data organization and query optimization, allowing users to navigate and analyze data through business concepts rather than just structural attributes. The domain hierarchy adds another layer of abstraction that simplifies complex queries.
2Productivity
If data domains are separated and organized hierarchically, then query efficiency improves, but the system complexity increases
Solution Approach 1:
The patent segments the data cube into multiple independent data domains that can be updated and maintained separately. Each domain represents a coherent business concept (e.g., product domain, customer domain, time domain) that can be processed independently. This segmentation enables partial updates without requiring full data cube regeneration, significantly improving update efficiency while maintaining manageable complexity through clear domain boundaries.
Solution Approach 2:
The patent implements a hierarchical domain structure where domains are nested within parent domains, forming a tree-like organization. Child domains inherit properties from parent domains and can be further subdivided. This nesting principle allows the system to manage complexity by organizing domains at appropriate levels of abstraction, enabling efficient queries through inheritance while maintaining a structured, manageable hierarchy rather than flat complexity.
3Ease of operation
If multidimensional data is organized without data domains, then the system structure remains simple, but querying and aggregation become inefficient for large datasets
Solution Approach 1:
The patent performs preliminary organization of multidimensional data into data domains during the data cube creation process. This preliminary action establishes the domain hierarchy and relationships before querying occurs, enabling efficient query execution without requiring complex runtime processing. The domains are pre-defined with their attributes, measures, and relationships, so queries can directly leverage this pre-organized structure rather than building it on-the-fly.
Solution Approach 2:
The patent changes the organizational parameters of the data cube by introducing data domains as a new parameter for data classification and arrangement. Instead of organizing data solely by traditional OLAP dimensions, the system now uses domain parameters that reflect business concepts and relationships. This parameter change enables more intuitive and efficient querying by aligning the data organization with business logic rather than just structural attributes.
Data Source
AI summary
Systems and method for creating multidimensional data cubes containing data domains for analyzing large amounts of data are provided. Data domains may be included in the major object of a multidimensional data cube. Further embodiments of the present invention provide methods for querying multidimensional data cubes having data domains. Embodiments of the present invention provide for defining data domains by any object in the major object model and for defining parent and child data domains.