Data Preloading Technique using Intention Prediction

Lee, Seung Yup; Yoo, Juwan; Ju, Da Young

doi:10.1007/978-3-319-07227-2_4

Cited by 1 publication

(1 citation statement)

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“…However, they are not suitable for current web applications due to the complicated layout of hyperlinks and the excessive computational consumptions. Furthermore, web data prefetching based on the cursor's dwell time, velocity [ 23 ] and moving direction [ 24 , 25 ] was proposed to reduce the web access latency, but it often results in false prefetching due to the user's meaningless cursor movement.…”

Section: Introductionmentioning

confidence: 99%

Gaze-Assisted User Intention Prediction for Initial Delay Reduction in Web Video Access

Lee

Yoo

Han

2015

Sensors

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Despite the remarkable improvement of hardware and network technology, the inevitable delay from a user's command action to a system response is still one of the most crucial influence factors in user experiences (UXs). Especially for a web video service, an initial delay from click action to video start has significant influences on the quality of experience (QoE). The initial delay of a system can be minimized by preparing execution based on predicted user's intention prior to actual command action. The introduction of the sequential and concurrent flow of resources in human cognition and behavior can significantly improve the accuracy and preparation time for intention prediction. This paper introduces a threaded interaction model and applies it to user intention prediction for initial delay reduction in web video access. The proposed technique consists of a candidate selection module, a decision module and a preparation module that prefetches and preloads the web video data before a user's click action. The candidate selection module selects candidates in the web page using proximity calculation around a cursor. Meanwhile, the decision module computes the possibility of actual click action based on the cursor-gaze relationship. The preparation activates the prefetching for the selected candidates when the click possibility exceeds a certain limit in the decision module. Experimental results show a 92% hit-ratio, 0.5-s initial delay on average and 1.5-s worst initial delay, which is much less than a user's tolerable limit in web video access, demonstrating significant improvement of accuracy and advance time in intention prediction by introducing the proposed threaded interaction model.

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Section: Introductionmentioning

confidence: 99%