Context Data Compact Prediction Tree (CD-CPT): Transforming User Experience Through Predictive Analysis

Pooja Goyal, Md Khan, Natnael Teshome, Brendan Geary, Renee Bryce

2024

Abstract

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. Such context events may affect usage of the smartphone and smartphone applications by the user and the behaviour of these applications, Sparsity and complexity of these events make it difficult to identify patterns and trends in the data using traditional data mining techniques. Hence, predictive analysis of these events and finding patterns in context event data can have drastic impact on the application usage and enhance user experience. Prediction trees can be used to predict future events based on the context of past events, 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+. The experimental results indicate that AKOM and TDAG outperform other algorithms, achieving a 50.4% weighted F-1 score for the highest supported context event. CD-CPT, without referencing the test file, still achieves a 14.27% weighted F-1 score for the same event, showing potential for improved accuracy in predicting context data compared to other algorithm.

Paper Citation

in Harvard Style

Goyal P., Khan M., Teshome N., Geary B. and Bryce R. (2024). Context Data Compact Prediction Tree (CD-CPT): Transforming User Experience Through Predictive Analysis. In Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS; ISBN 978-989-758-699-6, SciTePress, pages 166-173. DOI: 10.5220/0012615800003705

in Bibtex Style

@conference{iotbds24,
author={Pooja Goyal and Md Khan and Natnael Teshome and Brendan Geary and Renee Bryce},
title={Context Data Compact Prediction Tree (CD-CPT): Transforming User Experience Through Predictive Analysis},
booktitle={Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS},
year={2024},
pages={166-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012615800003705},
isbn={978-989-758-699-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS
TI - Context Data Compact Prediction Tree (CD-CPT): Transforming User Experience Through Predictive Analysis
SN - 978-989-758-699-6
AU - Goyal P.
AU - Khan M.
AU - Teshome N.
AU - Geary B.
AU - Bryce R.
PY - 2024
SP - 166
EP - 173
DO - 10.5220/0012615800003705
PB - SciTePress