Dynamic Rule Query System for Data Cube Analysis
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Solution Overview
Problem
Existing query processing systems for analyzing large datasets in data cubes are inflexible, require significant reprogramming for dynamic rule creation, and take extensive time to process queries, leading to complexity and inefficiency.
Innovation Solution
A dynamic rule query system that uses a data cube and DAX queries to manage rule queries, allowing users to update and generate queries without extensive reprogramming, by selecting rules and data elements and executing DAX queries on the data cube.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If preset queries are used to analyze billions of transactions in a data cube, then the system can process large datasets, but the processing time takes up to six to twelve hours
Solution Approach 1:
The patent pre-calculates and stores aggregate metrics, trends, and patterns in the data cube before queries are executed. This preliminary preparation allows the system to quickly retrieve pre-computed results rather than calculating them in real-time, dramatically reducing query processing time from hours to minutes or seconds.
Solution Approach 2:
The system dynamically adjusts query processing by leveraging the multi-dimensional structure of the data cube, allowing flexible slicing and dicing of data along different dimensions. This dynamic capability enables the system to efficiently handle varying query requirements without reprocessing the entire dataset, improving both speed and adaptability.
2Reliability
If the system uses hundreds of queries and dataflows to detect patterns in records, then comprehensive analysis is achieved, but the system lacks flexibility
Solution Approach 1:
The data cube structure serves as a universal framework that can handle multiple types of queries and analysis operations simultaneously. By organizing data in a multi-dimensional cube with standardized dimensions and measures, the system provides a flexible interface for various analytical operations without requiring separate processing pipelines for each query type, thus achieving both reliability and adaptability.
Solution Approach 2:
The patent introduces additional analytical dimensions and aggregation levels in the data cube structure, allowing users to analyze data from multiple perspectives (time, category, region, etc.). This dimensional approach enables comprehensive pattern detection while maintaining flexibility, as users can navigate and filter data across different dimensions without being constrained to fixed query paths.
3Ease of manufacture
If multiple different tables are used to store records with different measures, metrics, labels, and parameters, then data organization is achieved, but join operations complicate the system
Solution Approach 1:
The patent merges multiple separate data tables into a unified data cube structure that integrates various measures, metrics, labels, and parameters into a single multi-dimensional framework. This consolidation eliminates the need for complex join operations between multiple tables, as all data is pre-integrated within the cube's dimensional structure, simplifying both data organization and query processing.
Solution Approach 2:
The data cube acts as an intermediary layer between raw data sources and analytical queries. By pre-aggregating and organizing data from multiple source tables into the cube's standardized dimensional model during the ETL (Extract, Transform, Load) process, the system mediates the complexity of join operations, allowing users to query integrated data without directly managing table joins.
4Adaptability or versatility
If new rules are created or existing rules are expanded in the system, then rule coverage is improved, but modifications require changes to underlying software code
Solution Approach 1:
The system enables self-service rule creation and modification through the data cube's inherent dimensional and measure structure. Users can define new analysis rules by simply selecting different dimension combinations, aggregation functions, and threshold values within the existing cube framework, without requiring software developers to modify the underlying code. This self-service capability allows rapid adaptation and expansion of rule coverage while maintaining system stability.
Data Source
AI summary
A dynamic rule query system is provided. The dynamic rule query system includes a memory device and at least one processor. The at least one processor is programmed to store a plurality of information in a data cube, receive a selection of one or more rules and one or more data elements of the data cube, generate a DAX (data analysis expressions) query based upon the one or more rules and the one or more elements of the data cube, execute the DAX query on the data cube to receive results, and analyze the results to determine whether or not to activate one or more alerts.