Data Analysis Engine Wizard for OLAP Query Generation
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Solution Overview
Problem
Existing OLAP systems face challenges in dynamically generating aggregations at various levels of detail in real-time, especially when pre-computed aggregations are not available or are outdated, requiring users to understand complex query languages like MDX.
Innovation Solution
A user-friendly application program interface (API) that guides users through a wizard-based approach to specify data sources, data representations, and view descriptions, transforming these inputs into multidimensional queries for OLAP servers, and generating reports based on the query results, including optional reference levels for context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users directly query OLAP cubes using complex query languages like MDX to retrieve and aggregate data, then data analysis capability is improved, but user operation complexity increases significantly
Solution Approach 1:
The patent introduces a reporting system as an intermediary layer between users and OLAP cubes. This reporting system provides a simplified interface that automatically translates user-friendly report parameters into complex multidimensional queries, shielding users from the complexity of query languages like MDX while maintaining full data analysis capability
Solution Approach 2:
The patent segments the complex data analysis process into distinct, manageable components: data source selection, parameter specification, automatic query generation, and report rendering. This segmentation allows users to interact with simple, discrete steps rather than mastering a complex query language
2Speed
If OLAP systems pre-compute aggregations at multiple levels to enable fast data retrieval, then data access speed is improved, but system memory requirements and complexity increase
Solution Approach 1:
The patent implements dynamic aggregation computation that adapts to user needs in real-time. The system computes aggregations on-demand at the specific levels and dimensions required by each query, rather than pre-computing and storing all possible aggregations. This dynamic approach maintains fast access speeds while reducing system complexity and resource requirements
Solution Approach 2:
The patent changes the parameter of aggregation computation from static pre-computation to dynamic on-demand computation. The system adjusts the aggregation level, scope, and timing based on the specific query parameters provided by users, enabling fast data retrieval without requiring complex pre-computed aggregation structures
Data Source
AI summary
A system and method of analyzing data receives a user-specified selection of a data source, a user-specified selection of a type of data representation, and a user-specified description of a view to be constructed in accordance with the user-specified selection of the type of data representation. An application program interface (API) function generates a multidimensional query based on the user-specified type of data representation and the user-specified description of the view to be constructed, and submits the multidimensional query to the an On-line Analytical Processing (OLAP) server. The application program interface (API) function receives a multidimensional response from the OLAP server containing results acquired by the multidimensional query of the data source, and generates the view based on the results contained in the multidimensional response and in accordance with the user-specified selection of the type of data representation and the user-specified description of the view.