Dynamic Data Perspective Analysis Across Heterogeneous Databases
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Solution Overview
Problem
Conventional data perspective analysis tools are limited to static cube data structures, restricting perspectives to specific software and hardware platforms, and fail to utilize data elements outside the parent cube, hindering business intelligence and analytics in large organizations with diverse databases.
Innovation Solution
A data perspective analysis system and method that uses a processor to obtain perspective criteria, gather master and transactional data, generate dynamic perspectives by applying criteria, compare them, and display results as animated visualizations, enabling flexible and dimensionally limitless analysis across various platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional cube data structures are used, then data perspective analysis can be performed within a defined framework, but the analysis is limited to specific software and hardware platforms and cannot utilize data elements outside the parent cube
Solution Approach 1:
The patent implements a universal data perspective framework that can operate across multiple software and hardware platforms. The system uses a standardized interface layer that abstracts platform-specific details, allowing the same data perspective analysis logic to function on different databases (relational, NoSQL, cloud-based) without requiring platform-specific implementations. This enables data elements from diverse sources to be integrated and analyzed together.
Solution Approach 2:
The patent introduces an intermediary data abstraction layer between the raw data sources and the analysis engine. This intermediary layer translates various data formats and structures into a unified perspective model, enabling seamless integration of data elements from outside the traditional cube structure while maintaining analytical consistency across different platforms.
2Adaptability or versatility
If static predetermined data arrays are used, then data perspectives have a clear defined structure, but the analysis capabilities are limited and cannot dynamically adapt to new data dimensions
Solution Approach 1:
The patent transforms static data arrays into dynamic data structures that can adapt to new dimensions and data types. The system allows data perspectives to be dynamically defined and modified at runtime, enabling analysts to add new dimensions, filters, and metrics without redefining the entire data structure. This dynamic approach maintains structural clarity while enabling flexible analysis capabilities.
Solution Approach 2:
The patent segments the data structure into modular components (dimensions, measures, hierarchies) that can be independently defined and configured. This segmentation allows the system to build complex data perspectives from simple, well-defined building blocks, maintaining structural organization while enabling dynamic adaptation to new analytical requirements through incremental addition of modular elements.
3Productivity
If perspectives are restricted to being within the cube data structure or subsets thereof, then data organization remains simple and manageable, but full utilization of business intelligence and analytics is hindered in large organizations with multiple data objects on different databases
Solution Approach 1:
The patent merges data from multiple disparate databases and data objects into a unified data perspective framework. The system consolidates data from relational databases, NoSQL stores, cloud services, and other sources into a coherent analytical model, enabling comprehensive business intelligence that leverages all available data assets across the organization while maintaining manageable structural organization.
Data Source
AI summary
A data perspective analysis method is provided. The method includes obtaining, using a processor, one or more perspective criteria stored in a memory device. The method includes gathering, at the processor, master data and transactional data associated with each of the one or more perspective criteria. The method includes generating, at the processor, one or more perspectives by applying the one or more perspectives criteria to the master data and the transactional data. The method includes comparing, at the processor, at least two perspectives, and displaying, on a display controlled by the processor, a result of the comparison of the at least two perspectives as a dynamic and animated visualization.