Knowledge Graphing Service for Data Analysis Preferences
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Solution Overview
Problem
Users face challenges in analyzing and leveraging large volumes of data across various sources and devices, particularly in workplace environments, due to the overwhelming amount of information and difficulty in sharing knowledge among coworkers, especially when switching between small and large form-factor devices.
Innovation Solution
The system identifies data sources related to user application usage, determines activity signals, and applies them to a knowledge graphing service to infer data analysis preferences, generating data insight objects that visualize target datasets and provide enhanced data analysis capabilities across different devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually analyze and manage large volumes of data across multiple sources, then data analysis completeness can be achieved, but user time and effort requirements increase significantly
Solution Approach 1:
The system automatically infers data analysis preferences by monitoring and analyzing user interactions with data analysis tools, eliminating the need for users to manually specify their preferences. The system self-updates preference profiles based on observed usage patterns, thereby reducing user effort while maintaining comprehensive data analysis capabilities.
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns and pre-establishes data analysis preference profiles before users need them. By proactively inferring preferences from ongoing interactions and preparing personalized analysis configurations in advance, the system reduces the time users would otherwise spend on data analysis setup and management.
2Adaptability or versatility
If users switch between small form-factor devices (smartphones, tablets) and large form-factor devices (desktops), then device flexibility is improved, but consistency of data analysis experience deteriorates
Solution Approach 1:
The system creates device-agnostic data analysis preference profiles that work uniformly across smartphones, tablets, and desktop computers. By inferring preferences from user behavior rather than device-specific configurations, the system ensures that analytical insights and preferences remain consistent regardless of which device the user is currently using, achieving both flexibility and stability.
3Productivity
If users attempt to leverage data and analysis of co-workers, then organizational knowledge utilization is improved, but difficulty in sharing and accessing colleague insights increases
Solution Approach 1:
The system merges individual user data analysis preferences into organizational-level preference profiles by analyzing aggregated user interactions. This combines multiple sources of analytical insights into unified organizational knowledge that can be easily accessed and applied across the organization, simplifying knowledge sharing while improving productivity.
Data Source
AI summary
Systems, methods, and software for developing application usage information are provided herein. An exemplary method includes identifying data sources that relate to application usage activity of one or more users, determining activity signals related to at least data analysis by the one or more users from among the application usage activity, and applying the activity signals to a knowledge graphing service that infers data analysis preferences from among the activity signals. The method also includes providing ones of the data analysis preferences for use by data insight services that establish data insight objects visualizing target datasets based at least on the ones of the data analysis preferences.