Virtual Multidimensional Cube Mapping for Enterprise Data Analysis
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Solution Overview
Problem
Business enterprises face difficulties in analyzing large datasets due to the complexity of maintaining and relating disparate database tables and multidimensional data structures, making it challenging to detect and analyze relationships between data from different sources.
Innovation Solution
The system automatically creates mappings between data in different cubes, allowing for the addition of mapped dimensions and measures to a source fact table, enabling the execution of complex queries without the need for explicit joins or new multidimensional data cubes, thereby simplifying the analysis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple disparate database tables and multidimensional data structures are maintained to store and analyze large amounts of enterprise data, then the system can store and process diverse data types, but the complexity of detecting and analyzing relationships between data in different tables and structures increases significantly
Solution Approach 1:
The patent introduces a virtual multidimensional cube as an intermediary layer between the source fact table and mapped fact tables. This virtual cube contains mapped dimensions and mapped measures that automatically join multiple data sources, allowing users to query relationships across disparate tables without manually creating physical joins or understanding the underlying data structure complexity.
2Quantity of substance
If additional database tables and multidimensional data structures are defined to accommodate growing enterprise data, then the system can handle increasing data volume, but it becomes difficult for users to remain up-to-date regarding all available tables and structures
Solution Approach 1:
The patent creates a virtual copy of the source fact table by adding mapped dimensions and mapped measures to it. This virtual representation allows users to work with a single familiar table structure while the system automatically retrieves and processes data from multiple underlying fact tables, eliminating the need for users to learn and track multiple complex data structures.
3Measurement precision
If explicit joins or new multidimensional data cubes are created to execute queries across multiple data sources, then accurate relationship analysis can be achieved, but the complexity of the analysis process increases
Solution Approach 1:
The patent pre-establishes the relationships between source and mapped fact tables by defining mapped dimensions and mapped measures in advance. These mappings are automatically configured to join the appropriate tables and apply the correct join conditions, so when users execute queries, the complex join logic has already been prepared and they only need to specify their query requirements without dealing with join complexity.
Data Source
AI summary
A method includes receiving, at a server from a computing device, a query identifying a source multidimensional cube, a mapped measure associated with the source multidimensional cube, and one or more classification values. The mapped measure corresponds to a measure associated with a mapped multidimensional cube that is distinct from the source multidimensional cube. The method also includes generating a cell set, wherein each cell of the cell set corresponds to a respective combination of the one or more classification values. The method further includes executing the mapped measure with respect to each cell of the cell set to generate one or more query execution results and sending the one or more query execution results to the computing device.