Weighted Scroll for Hierarchical Data Preloading
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Solution Overview
Problem
In distributed computing environments, retrieving data from hierarchical structures can cause delays due to the lack of prediction on user interest, leading to inefficient data fetching and customer experience impact.
Innovation Solution
Systems and methods analyze user activity to predict data objects of interest and preload them by calculating urgency scores for nodes in the user interface, dynamically updating scores based on user interaction, and loading additional data when thresholds are met, thereby reducing load times and unnecessary transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is fetched in batches as needed, then network bandwidth is conserved, but user experience deteriorates due to load times and delays
Solution Approach 1:
The system performs preliminary actions by predicting which data objects the user will navigate to next and preloading them into the buffer before the user actually requests them. This is achieved by monitoring user interactions, calculating urgency scores for displayed nodes, and automatically fetching additional data when thresholds are met, thereby eliminating load times during user navigation.
Solution Approach 2:
The system dynamically adjusts data loading behavior based on real-time user interactions. Urgency scores are calculated and updated as users navigate, and the threshold for triggering data loading is dynamically determined based on node depth and other factors. This dynamic approach optimizes the balance between preloading data and conserving network bandwidth.
2Ease of operation
If all data is preloaded upfront, then user navigation is seamless, but network bandwidth is wasted on unnecessary data transfers
Solution Approach 1:
Instead of uniformly preloading all data, the system applies local quality by selectively preloading only those data objects that are predicted to be of interest to the user. Urgency scores are calculated for individual nodes based on user interactions, and data loading is triggered selectively when thresholds are met for specific nodes, rather than loading everything indiscriminately.
Solution Approach 2:
The system changes parameters dynamically by adjusting urgency scores and loading thresholds based on user behavior patterns, node depth, and interaction history. This allows the system to adapt the preloading strategy to match actual user needs, avoiding the waste of bandwidth while maintaining smooth navigation.
3Productivity
If data loading threshold is set low, then data is loaded proactively, but unnecessary data requests increase
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring user interactions and adjusting urgency scores accordingly. The threshold for data loading is dynamically determined based on node depth and user behavior patterns, creating a feedback loop that refines the loading strategy over time and reduces unnecessary transactions while maintaining data availability.
4Measurement precision
If urgency scores are calculated for all nodes, then prediction accuracy improves, but computational overhead increases
Solution Approach 1:
The system segments the computational effort by calculating urgency scores selectively for nodes that are currently displayed or likely to be displayed, rather than for all nodes in the entire data structure. This segmentation reduces computational overhead while maintaining prediction accuracy for the relevant subset of nodes.
Data Source
AI summary
In various examples, a first dataset including a first set of nodes maintained in a hierarchical data structure is obtained and displayed in a user interface. Urgency scores are then determined for a subset of nodes of the set of nodes displayed in the user interface, the urgency score indicating that the portion of the first dataset displayed in the user interface is within a distance to an end of the first data set, and based on the urgency score being above a threshold, causing a request for a second dataset including a second set of nodes maintained in the hierarchical data structure to be generated.