Multidimensional Query Decomposition for Relational Data Sources
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Solution Overview
Problem
Current data analysis technologies face limitations in converging multidimensional and relational data access, as existing systems lack a mechanism to effectively process and decompose multidimensional queries against relational data sources, leading to inefficiencies in data analysis and reporting.
Innovation Solution
A method is introduced that intercepts multidimensional queries, identifies tabular sub-expressions, extracts information, creates cube items, and replaces sub-expressions with references to these items, generating decomposed sub-queries to execute queries efficiently across relational data sources, thereby bridging the gap between multidimensional and relational technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If MOLAP engine is used to provide fast multidimensional query responses, then query response speed is improved, but device complexity increases due to cube structure requirements
Solution Approach 1:
The patent introduces a query decomposition mechanism that acts as an intermediary between the relational database and the OLAP query interface. The decomposition engine breaks down complex multidimensional queries into simpler sub-queries that can be executed directly against relational tables, eliminating the need for pre-computed cube structures while maintaining fast response times.
Solution Approach 2:
The patent segments complex multidimensional queries into multiple tabular sub-queries that can be independently executed against relational data sources. Each sub-query handles a specific aspect of the original query, and the results are subsequently aggregated to form the final multidimensional view, replacing the need for monolithic cube structures.
2Adaptability or versatility
If ROLAP engine is used to handle large number of attributes, then adaptability is improved, but query processing time increases due to complex SQL statements
Solution Approach 1:
The patent implements a dynamic query decomposition approach that adapts to the specific characteristics of each incoming query. The system dynamically determines the optimal decomposition strategy based on the query's attributes, dimensions, and measures, generating efficient execution plans that minimize processing time while maintaining full adaptability to handle any number of attributes.
3Ease of operation
If multidimensional query language functionality is provided, then ease of operation is improved, but device complexity increases due to lack of convergence with relational technology
Solution Approach 1:
The patent creates a universal query processing framework that can handle both traditional relational queries and multidimensional OLAP queries through a single unified system. The decomposition engine translates multidimensional query operations into relational SQL operations, allowing the same infrastructure to serve multiple functions without requiring separate specialized systems.
Data Source
AI summary
A multidimensional operation provider is provided for processing a multidimensional query against a relational datasource. The multidimensional operation provider comprises a query interceptor, a query decomposer and a query replacer. The query interceptor detects and intercepts a multidimensional query that is issued against one or more relational data sources. The query decomposer decomposes expressions of the multidimensional query into one multidimensional expression and/or one or more relational expressions. The query replacer replaces the multidimensional query with a stack of operations expressed by the decomposed multidimensional expressions and/or the relational expressions.