Context-Sensitive Cluster Analysis for Business Intelligence
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Solution Overview
Problem
Traditional Business Intelligence (BI) systems are complex, difficult to deploy and customize, and struggle with unstructured data, making them costly and resource-intensive, while lacking the ability to analyze the impact of populations on calculated metrics, which hinders organizations from gaining competitive business intelligence.
Innovation Solution
The development of apparatus, systems, and methods for dynamic on-demand context-sensitive cluster analysis in BI systems, allowing for real-time analysis of multi-dimensional data sets by selecting relevant dimension members, computing dimensional scores, and ranking them based on influence on metrics, using cloud-based computing and client-server architectures to facilitate cost-effective and resource-efficient intelligence generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional BI systems are used to process and analyze organizational data, then calculations of various metrics can be performed, but the system complexity and deployment difficulty increase significantly
Solution Approach 1:
The patent extracts the core analytical function from complex traditional BI systems by implementing a streamlined dimension member scoring system that directly computes influence scores without requiring the full overhead of traditional BI infrastructure. This separates the essential metric analysis capability from the surrounding complexity.
Solution Approach 2:
The system automatically determines current context and selects relevant dimension members without requiring manual configuration or complex system setup. The automated context determination and dynamic selection processes enable the system to serve itself, reducing deployment complexity while maintaining metric calculation precision.
2Adaptability or versatility
If traditional BI systems are customized for specific applications, then department-specific needs can be met, but the deployment difficulty and resource requirements increase
Solution Approach 1:
The system dynamically adapts to different departmental needs by automatically determining current context and selecting relevant dimension members based on the specific application scenario. This dynamic adaptation eliminates the need for static customization while maintaining department-specific relevance, making deployment easier while preserving adaptability.
Solution Approach 2:
The dimension member scoring system serves multiple departmental functions through a single unified approach. By computing influence scores based on contextual relevance, the same core mechanism handles diverse departmental requirements without requiring separate customized deployments, thereby improving ease of operation while maintaining versatility.
3Loss of information
If comprehensive data analysis is performed on all dimension members, then complete metric impact assessment is achieved, but the computational resources and time required increase
Solution Approach 1:
The system performs partial analysis by focusing computational resources only on dimension members relevant to the current context. By computing influence scores selectively for context-relevant dimension members rather than all dimension members, the system achieves sufficient metric impact assessment without the time cost of comprehensive analysis of every possible dimension member.
Solution Approach 2:
The analysis process is segmented into context determination, relevant dimension member selection, and focused scoring computation. This segmentation allows the system to divide the comprehensive analysis task into manageable stages, processing only the necessary subset of dimension members while preserving the ability to assess complete metric impact when needed.
4Loss of energy
If cloud-based computing is used for on-demand analysis, then infrastructure costs are reduced, but the need for real-time context determination and dynamic selection increases system complexity
Solution Approach 1:
The cloud-based system automatically determines current context and selects relevant dimension members through automated processes without requiring complex manual configuration or infrastructure management. This self-service approach handles the contextual complexity internally while presenting a simplified interface, reducing infrastructure costs while managing complexity through automation rather than manual processes.
Data Source
AI summary
A particular method includes selecting a subset of a plurality of dimension members of a multi-dimensional data set. The method also includes computing a plurality of dimensional scores for the dimension members in the selected subset. Each dimensional score is associated with a particular dimension member in the subset and is a measure of a dimensional influence of the associated dimension member on a metric associated with the multi-dimensional data set. A dimension member with greater dimensional influence affects a value of the metric over a population more than a dimension member with less dimensional influence. The method further includes ranking the dimension members in the selected subset based on the dimensional scores.