Dynamic Data Cube for Real-Time Visualization
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Solution Overview
Problem
Existing data analytics systems require significant manual effort and technical expertise for data preparation and visualization, as they need input data to be transformed and loaded into a data warehouse before analysis can occur, limiting user flexibility and efficiency.
Innovation Solution
A real-time data visualization system that generates a dynamic data cube on demand, allowing users to select data elements and specify transforms, enabling immediate data manipulation and visualization without pre-processing, using a data cube processor to optimize data processing based on user requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is prepared using ETL tools before visualization, then data quality and consistency are improved, but manual effort and technical expertise requirements increase
Solution Approach 1:
The system automatically performs data preparation tasks including extraction, transformation, and loading without requiring manual configuration. The intelligent data preparation system self-adapts to user requests and autonomously executes ETL processes, eliminating the need for users to have technical expertise in data preparation while maintaining data quality.
Solution Approach 2:
The system pre-processes and prepares data in advance by creating virtual data cubes that store transformation logic and data relationships. This preliminary action allows data to be ready for visualization immediately when requested, eliminating the need for manual ETL configuration at the time of use while ensuring data quality through pre-validate transformations.
2Adaptability or versatility
If data is loaded into a data warehouse before analysis, then data analysis capability is improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The system introduces a virtual data cube as an intermediary layer between raw data sources and visualization tools. This virtual cube contains pre-computed transformation logic and data relationships, enabling complex data analysis capabilities without requiring a physical data warehouse infrastructure. The virtual cube mediates between data sources and analysis requests, simplifying the overall system architecture.
Solution Approach 2:
The virtual data cube serves multiple functions: it stores raw data, applies transformations, maintains data relationships, and provides data for visualization. This multi-functional component replaces the need for separate ETL tools, data warehouse infrastructure, and analysis tools, reducing system complexity while maintaining versatile data analysis capabilities.
3Adaptability or versatility
If manual ETL processes are used for data preparation, then data transformation flexibility is improved, but processing time and productivity decrease
Solution Approach 1:
The system pre-computes and stores transformation logic in virtual data cubes during system initialization or data loading. When visualization requests are made, the pre-prepared transformation logic is automatically executed without requiring manual ETL processes, maintaining transformation flexibility through configurable virtual cubes while dramatically reducing processing time.
Solution Approach 2:
The system replaces manual mechanical ETL processes with automated computational processes. The virtual data cube contains programmable transformation logic that automatically executes based on user requests, substituting manual configuration and execution with automated computational operations that maintain flexibility while improving processing speed and productivity.
4Manufacturing precision
If pre-processing data before visualization is performed, then visualization accuracy is improved, but user flexibility and efficiency decrease
Solution Approach 1:
The system makes the data preparation process dynamic and adaptive to user requests. Instead of fixed pre-processing, the virtual data cube allows users to dynamically select data elements, measures, and dimensions at the time of visualization requests. The transformation logic automatically adapts to user preferences while maintaining data accuracy through pre-validates transformation rules stored in the virtual cube.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with visualizations inform subsequent data preparation. The virtual data cube learns from user preferences and automatically adjusts transformation logic to match user expectations, maintaining visualization accuracy while enhancing user flexibility through adaptive, feedback-driven data preparation that responds to actual user needs rather than following rigid pre-defined processes.
Data Source
AI summary
Real-time data visualization systems and methods are described. A data cube may be generated, wherein the data cube comprises a set of transforms to be applied to two or more data elements from disparate data sources, wherein processing of the data cube is to result in a data cube result having a plurality of measures and one or more hierarchies. A user may build a visualization, which produces a visualization request associated with the data cube, the visualization request specifying one or more of the measures and hierarchies of the data cube. The system identifies, based on the visualization request, one or more transforms within the data cube to remove from the data cube for the purpose of the visualization request to produce a modified data cube, and carries out the transforms of the modified data cube to produce a modified data cube result, which is exposed to the client-side visualization processor for rendering the visualization.