Mobile Data Insight Framework for Cross-Device Visualization
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Solution Overview
Problem
Users face challenges in effectively analyzing and visualizing large volumes of data across various devices, particularly due to the difficulty in leveraging organizational knowledge and data changes, especially when switching between small and large form-factor devices, and in managing the sheer quantity of data sources.
Innovation Solution
A mobile data visualization framework that determines data insight candidates based on user preferences and processing lineage, generates insight views, and adapts presentation detail levels according to device properties, enabling seamless data analysis and visualization across different devices by employing insight services that process datasets and provide dynamic insight objects with metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data analysis is performed on large volumes of data across multiple devices, then data insight completeness is improved, but user ability to leverage organizational knowledge deteriorates
Solution Approach 1:
The patent segments data analysis by creating separate insight views for different device form factors (mobile vs. desktop). Mobile devices receive simplified, high-level insights while desktop devices receive comprehensive detailed analyses. This segmentation allows organizational knowledge to be preserved in the system while delivering appropriate portions to each device type, resolving the contradiction between handling large data volumes and maintaining knowledge accessibility.
Solution Approach 2:
The patent changes the parameter of presentation detail level based on device properties. Mobile devices receive insights with lower detail levels and simplified visualizations, while desktop devices receive full-detail insights with comprehensive data. This parameter adjustment enables the system to handle large data volumes effectively while ensuring organizational knowledge is appropriately delivered to devices capable of processing and displaying it.
2Adaptability or versatility
If data analysis is performed across small and large form-factor devices, then device versatility is improved, but data presentation effectiveness deteriorates
Solution Approach 1:
The patent applies local quality by tailoring the quality and detail of insight presentations to specific device contexts. Mobile devices receive simplified, mobile-optimized visualizations with appropriate detail levels for small screens, while desktop devices receive comprehensive, detailed visualizations optimized for larger displays. This localized adaptation maintains versatility across devices while ensuring each receives effective, context-appropriate presentations.
Solution Approach 2:
The patent implements dynamic adaptation of insight presentations based on real-time device properties. The system dynamically adjusts presentation detail levels, visualization types, and data granularity according to the specific device being used (mobile vs. desktop). This dynamic approach enables seamless cross-device versatility while maintaining optimal data presentation effectiveness for each device type.
3Productivity
If comprehensive data insights are provided, then data analysis depth is improved, but device complexity increases
Solution Approach 1:
The patent extracts comprehensive data analysis processing from mobile devices and relocates it to server-based insight services. Mobile devices receive only the essential, processed insights appropriate for their capabilities, while the complex analysis computations are performed remotely on powerful servers. This extraction resolves the contradiction by providing deep data analysis results to mobile users without burdening the mobile devices with complex processing requirements.
Solution Approach 2:
The patent introduces server-based insight services as an intermediary between comprehensive data sources and mobile devices. This intermediary performs the complex data analysis computations and delivers simplified, appropriately-detailed insights to mobile devices. The intermediary layer enables deep data analysis productivity while shielding mobile devices from complexity, as the intermediary handles all complex processing and presents only essential results to the mobile device.
Data Source
AI summary
Systems, methods, and software for mobile data visualization frameworks are provided herein. An exemplary method includes, determining data insight candidates for presentation on the mobile computing device that describe analysis of a target dataset, determined based at least on data analysis preferences associated with processing one or more past datasets, and where each of the data insight candidates includes at least one insight object directed to the target dataset and is described by object metadata that indicates at least a processing lineage used to produce the at least one insight object. The method includes selecting a presentation detail level for displaying the data insight candidates on the mobile computing device based at least on properties of the mobile computing device, and generating one or more insight views for presentation on the mobile computing device.