User Activity Data Correlation for Management System Personalization
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Solution Overview
Problem
Management systems face challenges in adoption due to overhead and steep learning curves, particularly with search-centric user interfaces that require a large knowledge base of system capabilities, leading to limited personalization and flexibility in defining key performance indicators (KPIs) and reports.
Innovation Solution
A method for sharing user activity data involves collecting and analyzing user-defined system management queries to identify correlations, generating system management compilation data, and distributing it to subscriber entities, enabling flexible and personalized KPIs and reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a search-centric user interface with high flexibility is provided, then adaptability and personalization are improved, but device complexity and learning curve increase
Solution Approach 1:
The system performs preliminary analysis of user activity data to pre-identify correlations and patterns in query usage. By analyzing historical user behavior in advance, the system can proactively generate personalized KPIs and reports without requiring users to manually configure complex parameters, thus reducing the learning curve while maintaining adaptability
Solution Approach 2:
The system enables self-service personalization by automatically analyzing user activity records and generating personalized KPIs and reports based on identified correlations. Users benefit from automated customization without needing to understand the underlying complexity, as the system serves itself by learning from user behavior patterns
2Adaptability or versatility
If user-defined system management queries are allowed, then adaptability and versatility are improved, but overhead and processing complexity increase
Solution Approach 1:
The system merges multiple individual user queries into consolidated analysis by identifying correlations among queries. Instead of processing each query separately, the system combines related queries and their results, reducing redundant processing overhead while maintaining the flexibility of user-defined queries
Solution Approach 2:
The system creates universal compilation data that serves multiple purposes and user needs simultaneously. By analyzing correlations across different user queries, the system generates compilation data that can be reused across multiple contexts and user requirements, reducing overall processing overhead through multi-functional utilization
3Productivity
If personalized KPIs and reports are enabled, then usability and productivity are improved, but data processing requirements and system overhead increase
Solution Approach 1:
The system performs preliminary correlation analysis on user activity data to pre-identify patterns and relationships among queries. This advance analysis allows the system to generate personalized KPIs and reports more efficiently when requested, as the foundational correlation work has already been completed, reducing real-time data processing requirements
Solution Approach 2:
The system merges correlated user queries into unified analysis results, reducing redundant data processing. By combining processing of related queries into single compilation operations, the system maintains personalized report generation capability while reducing overall data processing volume through consolidation of correlated operations
Data Source
AI summary
Methods, systems, and computer readable mediums for sharing user activity data are disclosed. According to one example, a method includes receiving a collection of user activity records, wherein each of the user activity records is associated with at least one user-defined system management query and analyzing the collection of user activity records to identify one or more correlations existing among the user-defined system management queries. The method further includes generating system management compilation data based on the identified one or more correlations and distributing the system management compilation data to a plurality of subscriber entities.