TY - GEN
T1 - Mining temporal profiles of mobile applications for usage prediction
AU - Liao, Zhung Xun
AU - Lei, Po Ruey
AU - Shen, Tsu Jou
AU - Li, Shou Chung
AU - Peng, Wen-Chih
PY - 2012
Y1 - 2012
N2 - Due to the proliferation of mobile applications (abbreviated as Apps) on smart phones, users can install many Apps to facilitate their life. Usually, users browse their Apps by swiping touch screen on smart phones, and are likely to spend much time on browsing Apps. In this paper, we design an AppNow widget that is able to predict users' Apps usage. Therefore, users could simply execute Apps from the widget. The main theme of this paper is to construct the temporal profiles which identify the relation between Apps and their usage times. In light of the temporal profiles of Apps, the AppNow widget predicts a list of Apps which are most likely to be used at the current time. AppNow consists of three components, the usage logger, the temporal profile constructor and the Apps predictor. First, the usage logger records every App start time. Then, the temporal profiles are built by applying Discrete Fourier Transform and exploring usage periods and specific times. Finally, the system calculates the usage probability at current time for each App and shows a list of Apps with highest probability. In our experiments, we collected real usage traces to show that the accuracy of AppNow could reach 86% for identifying temporal profiles and 90% for predicting App usage.
AB - Due to the proliferation of mobile applications (abbreviated as Apps) on smart phones, users can install many Apps to facilitate their life. Usually, users browse their Apps by swiping touch screen on smart phones, and are likely to spend much time on browsing Apps. In this paper, we design an AppNow widget that is able to predict users' Apps usage. Therefore, users could simply execute Apps from the widget. The main theme of this paper is to construct the temporal profiles which identify the relation between Apps and their usage times. In light of the temporal profiles of Apps, the AppNow widget predicts a list of Apps which are most likely to be used at the current time. AppNow consists of three components, the usage logger, the temporal profile constructor and the Apps predictor. First, the usage logger records every App start time. Then, the temporal profiles are built by applying Discrete Fourier Transform and exploring usage periods and specific times. Finally, the system calculates the usage probability at current time for each App and shows a list of Apps with highest probability. In our experiments, we collected real usage traces to show that the accuracy of AppNow could reach 86% for identifying temporal profiles and 90% for predicting App usage.
KW - Data mining
KW - Mobile application
KW - Prediction
KW - Temporal profile
UR - http://www.scopus.com/inward/record.url?scp=84873141344&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2012.11
DO - 10.1109/ICDMW.2012.11
M3 - Conference contribution
AN - SCOPUS:84873141344
SN - 9780769549255
T3 - Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
SP - 890
EP - 893
BT - Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
T2 - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
Y2 - 10 December 2012 through 10 December 2012
ER -