Dynamic Semantic Tree Weighting for Business Intelligence Data Analysis
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Solution Overview
Problem
Business intelligence tools face challenges in efficiently analyzing and leveraging vast amounts of data due to the dynamic nature of data usage across multiple databases and varying user interests, making data analysis tedious and process-intensive.
Innovation Solution
The dynamic assignment of weights to underlying data elements based on user visits, allowing for the construction of local and group trees that can be used for various applications such as tag generation, improving data usage analysis by prioritizing relevant data elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data analysis is performed on vast amounts of business intelligence data spread across multiple databases, then comprehensive data coverage is achieved, but analysis complexity and processing time increase significantly
Solution Approach 1:
The patent segments the vast business intelligence data into hierarchical groups and subgroups, organizing data elements into a structured tree format. This segmentation allows the system to manage and analyze data in manageable portions rather than as a monolithic whole, reducing analysis complexity while maintaining comprehensive coverage.
Solution Approach 2:
The patent applies local quality by assigning different weights to different data elements based on their importance and relevance. High-weight elements represent more important data that require closer attention, while low-weight elements can be processed more efficiently. This differential weighting allows the system to focus computational resources on critical data while still maintaining overall data coverage.
2Productivity
If all data elements are analyzed with equal importance, then data neutrality is maintained, but processing efficiency decreases due to inability to prioritize
Solution Approach 1:
The patent implements dynamic weighting where the importance of data elements can change over time based on user interactions and business context. As users interact with the system and provide feedback, the weights of data elements are dynamically adjusted to reflect their actual importance. This dynamic approach maintains processing efficiency by continuously prioritizing relevant data while preserving adaptability to changing business needs.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with data elements provide information that feeds back into the weighting algorithm. When users frequently access or interact with certain data elements, the system learns from this behavior and increases the weight of those elements, thereby improving processing efficiency for future operations while adapting to actual data usage patterns.
3Loss of information
If manual analysis of data usage patterns is performed, then detailed insights can be obtained, but the process becomes extremely tedious and time-intensive
Solution Approach 1:
The patent implements self-service by enabling the system to automatically analyze data usage patterns and assign weights without requiring manual intervention. The system autonomously monitors user interactions, processes usage data, and updates element weights automatically. This self-service capability eliminates the need for tedious manual analysis while preserving detailed insights into data usage patterns, significantly reducing analysis time.
Solution Approach 2:
The system performs preliminary analysis by pre-processing and pre-weighting data elements based on initial usage patterns before full analysis is required. This preliminary action prepares the data structure in advance, so when detailed analysis is needed, the system already has organized data with preliminary weights, reducing the time required for comprehensive analysis while maintaining insight quality.
Data Source
AI summary
Various embodiments of systems and methods for dynamically weighted semantic trees are described herein. One or more software elements of a hierarchy are identified in response to user actions in a report. The user actions are related to at least one software element of the one or more software elements of the hierarchy. A local tree is constructed for each user by assigning local weights for the one or more software elements based on user visits to the one or more software elements. A group tree is constructed for each group to which the users belong by assigning group weights for the one or more software elements based on the user visits to the one or more software elements. The local tree and the group tree are stored for use in various applications.