Authors:
Pooja Goyal
1
;
Md Khan
1
;
Natnael Teshome
1
;
Brendan Geary
2
and
Renee Bryce
1
Affiliations:
1
Computer Science & Engineering, University of North Texas, Denton, Texas, U.S.A.
;
2
Computer Science, Florida Polytechnic University, Florida, U.S.A.
Keyword(s):
Context-Aware Applications, Sequence Prediction, Sequential Rule Mining, Compact Prediction Tree, Transition Directed Acyclic Graph, Prediction by Pattern Mining, All-k Order Markov, Dependency Graph, Android Testing, Context Aware Environments, Mobile Application Testing.
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 Compac
t 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.
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