Automatically extracting interaction and app data from mobile application traces

In this research, we used an existing system to collect mobile interaction traces and extract meaningful information in terms of interaction data, apps, and layout information and complexity of mobile apps. The preeminent driving force for this research was to come up with a system that is scalable and can be used to extract interactions and layouts from mobile apps, as well as enable us to make claims about the complexity of mobile apps and the flows that they offer. Throughout the course of this research, we collected Android mobile interaction traces and presented a technique which enables extraction of frequent interactive elements from the traces in an unsupervised manner using neural network auto-encoders and k-means clustering. The research work also enables us to find similar layouts across apps and make claims about the location of some of these interactive elements. This research provides a scalable data-driven approach to finding clusters of frequent icons and interactions as well as layouts.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2016

Author dc:creator

• Harish, Abhishek

Contributors dc:contributor

• Kumar, Ranjitha

• Unsupervised Clustering
• Human-computer interaction (HCI)
• Interaction Mining
• Element Extraction

Statement dc:rights

• Copyright 2016 Abhishek Harish

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/90757

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/90757