OLAP Vector Analysis Engine for Rapid Data View Generation
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Solution Overview
Problem
Current OLAP systems face inefficiencies in reorienting and analyzing large datasets within OLAP cubes, particularly in generating new views of data quickly, which can take hours with conventional relational databases and report writers, whereas OLAP cubes require rapid execution to avoid wasting analyst time.
Innovation Solution
A system and method for processing and analyzing vectors based on OLAP data, involving a base vector with a time dimension and other dimensions, a comparison vector, and a data analysis engine that applies operations to modify the base vector, allowing for efficient reorientation and analysis of data within OLAP cubes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional relational databases and report writers are used to analyze OLAP cube data, then data can be processed and analyzed, but the analysis time becomes excessively long (hours)
Solution Approach 1:
The patent introduces a specialized data analysis engine as an intermediary component that operates between the OLAP cube data storage and the user interface. This engine is specifically designed to handle multidimensional data efficiently, applying optimized algorithms for operations like drill-down, roll-up, and slice-and-dice that enable rapid analysis without requiring conventional relational database processing speeds.
Solution Approach 2:
The patent changes the fundamental parameters of data processing by transitioning from row-by-row processing in conventional databases to pre-aggregated multidimensional data structures in OLAP cubes. The system processes data at multiple levels of aggregation simultaneously, changing the processing granularity from individual records to pre-computed summary statistics, thereby dramatically reducing analysis time.
2Productivity
If OLAP cubes are used to store multidimensional data, then data can be reoriented and analyzed quickly, but the system complexity increases
Solution Approach 1:
The patent segments the data analysis functionality into distinct operational components: base vector definition, comparison vector configuration, analysis operation application, and result rendering. Each component handles a specific aspect of the analysis process, making the overall complex system more manageable and easier to implement while maintaining high performance through specialized processing at each stage.
3Measurement precision
If detailed cell-by-cell analysis is performed on OLAP cube data, then precise insights can be obtained, but the processing time increases significantly
Solution Approach 1:
The patent applies preliminary actions by pre-calculating and pre-aggregating data at multiple levels before the user needs analysis. The OLAP cube structure stores pre-computed summary statistics for various dimensions and hierarchies, so when analysis is required, the system can quickly retrieve and process pre-aggregated data rather than computing everything from raw records, enabling both detailed cell-level analysis and rapid execution.
Data Source
AI summary
A system and method for processing a base vector derived from data stored in an OLAP cube. The system comprises a component configured for defining a base vector and a comparison vector, wherein the comparison vector comprises one or more dimensions corresponding to dimensions of the base vector. The system includes a component configured for defining a time scope associated with the base vector. The system further comprises a component configured for performing one or more analysis operations on the comparison vector to generate comparison values and a component configured for rendering the base and comparison vectors. According to another aspect, the system includes a component for altering or modifying individual data cells in the base vector and/or parameters associated with the base vector and/or analysis operation.