Data Preloading Technique using Intention Prediction

Various smart devices provide fast response time and ubiquitous web-environment to users for better user experiences (UXs). However, high device performance that users perceive is not always promised because there should be limited network bandwidth, and computation capabilities. When the network and computation capabilities are overloaded, users experience buffering and loading time to accomplish a certain task. We, therefore, propose data preloading technique [1], which predicts user intention and preloads the web and local application data to provide better device performance in spite of poor network conditions and outdated hardware. We also design intention cognitive model to predict user intention precisely. Four user intention prediction algorithms, which are applicable to various conventional input methods, are described and compared each performance in both user’s and device’s aspects.