Logical Data Model Modification for Customized Analytics
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Solution Overview
Problem
Traditional Business Intelligence technologies are costly, require specialized skills, and struggle to process large volumes of data quickly, failing to provide customized insights that align with user needs and are not adapted for specialized applications, leading to inefficiencies and restricted feature sets.
Innovation Solution
The system modifies logical data models for relational multi-dimensional analytic databases based on user input and performs online analytical processing to provide customized views of data, allowing for roll-up, drill-down, slicing, and dicing operations, and includes modules for data retrieval, normalization, and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional Business Intelligence technologies are used, then data analysis capability is provided, but procurement costs are high and specialized skills are required
Solution Approach 1:
The system enables users to perform data analysis through intuitive drag-and-drop operations without requiring specialized technical skills. Users can create custom analyses by simply selecting data sources and defining parameters through graphical interfaces, eliminating the need for complex SQL queries or specialized BI knowledge while maintaining powerful analytical capabilities
Solution Approach 2:
The system introduces an intermediary layer between users and complex data processing operations. This intermediary provides automated data retrieval, normalization, and analysis functions that translate simple user actions into complex underlying operations, hiding the complexity while delivering powerful results
2Productivity
If traditional Business Intelligence technologies are used, then data analysis is performed, but processing speed for large volumes of data is insufficient
Solution Approach 1:
The system segments large datasets into manageable portions and processes them in parallel using distributed computing architecture. Data is divided across multiple processing nodes that simultaneously analyze different portions of the data, enabling fast processing of large volumes while maintaining results accuracy through aggregation of segment outputs
3Adaptability or versatility
If traditional Business Intelligence technologies are used, then general data analysis is provided, but customization for specialized applications is limited
Solution Approach 1:
The system provides dynamic customization where users can adapt data models, analysis parameters, and visualizations in real-time based on their specific needs. The system automatically adjusts its behavior based on user selections and data characteristics, enabling specialized application adaptation without requiring complex manual configuration of underlying systems
Data Source
AI summary
A computer-implemented method for performing customized large-scale data analytics may include (1) providing a logical-data-model user interface to enable modifying a logical data model of a relational multi-dimensional analytic database, (2) receiving, via the logical-data-model user interface, user input to modify the logical data model of the relational multi-dimensional analytic database, (3) modifying the logical data model of the relational multi-dimensional analytic database based on the user input, (4) providing a visualization user interface, based on the logical data model, to enable performing online analytical processing operations, and (5) receiving, via the visualization user interface, a request to perform an online analytical processing operation that provides a view of data stored within the relational multi-dimensional analytic database in accordance with the logical data model. Various other methods, systems, and computer-readable media are also disclosed.