Understanding usage states on mobile devices

UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Volumn , Issue , 2015, Pages 1221-1225

  Jesdabodi, Chakajkla ^a   Maalej, Walid ^a  

^a UNIVERSITY OF HAMBURG   (Germany)

Author keywords

Apps; Behavioral Profiles; Intent Identification; Usage Data

Indexed keywords

APPLICATION PROGRAMS; MOBILE DEVICES; SIGNAL ENCODING; SMARTPHONES; UBIQUITOUS COMPUTING;

BEHAVIORAL PROFILES; BEHAVIORAL STUDIES; CLUSTERING TECHNIQUES; CONTEXT AWARE SERVICES; CONTEXTUAL RECOMMENDATIONS; TOPIC MODELING; USAGE DATA; USAGE EXPERIENCE;

BEHAVIORAL RESEARCH;

EID: 84960938395     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2750858.2805837     Document Type: Conference Paper

References (21)

1. Rakesh Agrawal and Ramakrishnan Srikant. 1994. Fast Algorithms for Mining Association Rules in Large Databases. In Proceedings of 20th International Conference on Very Large Data Bases (VLDB94). 487-499.
2. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research 3 (2003), 993-1022. http://www.jmlr.org/papers/v3/blei03a.html
3. Matthias Böhmer, Brent Hecht, Johannes Schöning, Antonio Krüger, and Gernot Bauer. 2011. Falling asleep with Angry Birds, Facebook and Kindle: A large scale study on mobile application usage. In Proceedings of the 13th Conference on Human-Computer Interaction with Mobile Devices and Services. 47-56.
4. Trinh Minh Tri Do, Jan Blom, and Daniel Gatica-Perez. 2011. Smartphone Usage in the Wild: A Large-scale Analysis of Applications and Context. In Proceedings of the 13th International Conference on Multimodal Interfaces (ICMI 11). ACM, 353-360. DOI: http://dx.doi.org/10.1145/2070481.2070550
5. Trinh-Minh-Tri Do and Daniel Gatica-Perez. 2010. By Their Apps You Shall Understand Them: Mining Large-scale Patterns of Mobile Phone Usage. In Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia (MUM 10). ACM, Article 27, 10 pages.
6. Nathan Eagle and AlexSandy Pentland. 2009. Eigenbehaviors: identifying structure in routine. Behavioral Ecology and Sociobiology 63, 7 (2009), 1057-1066. DOI: http://dx.doi.org/10.1007/s00265-009-0739-0
7. Hossein Falaki, Ratul Mahajan, Srikanth Kandula, Dimitrios Lymberopoulos, Ramesh Govindan, and Deborah Estrin. 2010. Diversity in Smartphone Usage. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (MobiSys 10). ACM, 179-194.
8. E. Guzman and W. Maalej. 2014. How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews. In Requirements Engineering Conference (RE), 2014 IEEE 22nd International. 153-162. DOI: http://dx.doi.org/10.1109/RE.2014.6912257
9. Mark A. Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. 2009. The WEKA data mining software: An update. SIGKDD Explorations 11, 1 (2009), 10-18. DOI: http://dx.doi.org/10.1145/1656274.1656278
10. M. Harman, Yue Jia, and Yuanyuan Zhang. 2012. App store mining and analysis: MSR for app stores. In Proc. of Working Conference on Mining Software Repositories-MSR 12. 108-111. DOI: http://dx.doi.org/10.1109/MSR.2012.6224306
11. Walid Maalej, Hans-Jörg Happel, and Asarnusch Rashid. 2009. When users become collaborators: Towards continuous and context-Aware user input. In Proceeding of the 24th ACM SIGPLAN conference companion on Object oriented programming systems languages and applications (OOPSLA 09). ACM, 981-990. DOI: http://dx.doi.org/10.1145/1639950.1640068
12. Walid Maalej and Dennis Pagano. 2011. On the Socialness of Software. In Proceedings of the International Software on Social Computing and its Applications (2011-01-01). IEEE Computer Society.
13. Andrew Kachites McCallum. 2002. MALLET: A Machine Learning for Language Toolkit. http://mallet.cs.umass.edu
14. Abhinav Parate, Matthias Böhmer, David Chu, Deepak Ganesan, and Benjamin M. Marlin. 2013. Practical Prediction and Prefetch for Faster Access to Applications on Mobile Phones. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 13). ACM, 275-284. DOI: http://dx.doi.org/10.1145/2493432.2493490
15. Clayton Shepard, Ahmad Rahmati, Chad Tossell, Lin Zhong, and Philip T. Kortum. 2010. LiveLab: measuring wireless networks and smartphone users in the field. SIGMETRICS Performance Evaluation Review 38, 3 (2010), 15-20.
16. Choonsung Shin, Jin-Hyuk Hong, and Anind K. Dey. 2012. Understanding and Prediction of Mobile Application Usage for Smart Phones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp 12). ACM, 173-182. DOI: http://dx.doi.org/10.1145/2370216.2370243
17. Pang-Ning Tan, Michael Steinbach, and Vipin Kumar. 2005. Introduction to Data Mining. Addison-Wesley.
18. Svitlana Vakulenko, Oliver Müller, and Jan vom Brocke. 2014. Enriching iTunes App Store Categories via Topic Modeling. In Proceedings of the 35th International Conference on Information Systems.
19. Hannu Verkasalo. 2009. Contextual patterns in mobile service usage. Personal and Ubiquitous Computing 13, 5 (2009), 331-342.
20. Qiang Xu, Jeffrey Erman, Alexandre Gerber, Zhuoqing Morley Mao, Jeffrey Pang, and Shobha Venkataraman. 2011. Identifying diverse usage behaviors of smartphone apps. In Internet Measurement Conference. 329-344.
21. Tingxin Yan, David Chu, Deepak Ganesan, Aman Kansal, and Jie Liu. 2012. Fast App Launching for Mobile Devices Using Predictive User Context. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys 12). ACM, 113-126.