Context Data Compact Prediction Tree (CD-CPT): Transforming User Experience Through Predictive Analysis
Pooja Goyal, Md Khan, Natnael Teshome, Brendan Geary, Renée C. Bryce · 2024
This article asserts that use of IoT (Internet of Things) devices have significantly increased over the last decade, specifically smartphones as compared to desktops, and laptops have become an integral part of our everyday lives. Smartphone applications operate in dynamic environments and generate huge and vast amount of context events such as screen orientation, location, battery life, and network connectivity throughout the day. This work proposes a modified method of Compact Prediction Tree (CPT) called Context Data Compact Prediction Tree (CD-CPT) to predict real-world context data for multiple users. The experiments conducted used Transition Directed Acyclic Graph (TDAG) and All-k Order Markov (AKOM) algorithms to generate short-term predictions based on current context events and compare with baseline models such as Prediction by Pattern Mining (PPM), Dependency Graph (DG), CPT, and CPT+.