Abstract
The goal of this work is a unified approach for collecting data about user actions on mobile devices in an appropriate granularity for user modeling. To fulfill this goal, we have designed and implemented a framework for mobile user activity logging on Windows Mobile PDAs based on the MyExperience project. We have extended this system with hardware and software sensors to monitor phone calls, messaging, peripheral devices, media players, GPS sensors, networking, personal information management, web browsing, system behavior and applications usage. It is possible to detect when, at which location and how a user employs an application or accesses certain information, for example. To evaluate our framework, we applied it in several usage scenarios. We were able to validate that our framework is able to collect meaningful information about the user. We also outline preliminary work on analyzing the logged data sets.
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References
Brusilovsky, P., Maybury, M.T.: From Adaptive Hypermedia to the Adaptive Web. Communications of the ACM 45(5), 30–33 (2002)
Subramanya, S.R., Yi, B.K.: Enhancing the User Experience in Mobile Phones. IEEE Computer 40(12), 114–117 (2007)
Froehlich, J., Chen, M., Consolvo, S., Harrison, B., Landay, J.: MyExperience: A System for In situ Tracing and Capturing of User Feedback on Mobile Phones. In: Proc. of MobiSys Conf., San Juan, Puerto Rico (2007)
Chernov, S., Demartini, G., Herder, E., Kopycki, M., Nejdl, W.: Evaluating Personal Information Management Using an Activity Logs Enriched Desktop Dataset. In: Proc. of 3rd Personal Information Management Workshop (PIM 2008), CHI Conf., Florence, Italy (2008)
Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining Interesting Locations and Travel Sequences From GPS Trajectories. In: Proc. of International Conference on World Wide Web (WWW 2009), Madrid, Spain, pp. 791–800. ACM Press, New York (2009)
Choudhury, T., et al.: The Mobile Sensing Platform: An Embedded Activity Recognition System. IEEE Pervasive Computing 7(2), 32–41 (2008)
Jeong, J., Won, J., Bae, C.: User Activity Recognition and Logging in Distributed Intelligent Gadgets. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Seoul, Korea (2008)
Kanjo, E., Bacon, J., Roberts, D., Landshoff, P.: MobSens: Making Smart Phones Smarter. IEEE Pervasive Computing 8(4), 50–57 (2009)
MyExperience project, http://myexperience.sourceforge.net/ (accessed, June 2010)
Kang, J.H., Welbourne, W., Stewart, B., Borriello, G.: Extracting Places From Traces of Locations. ACM SIGMOBILE Mobile Computing and Communications Review 9(3), 58–68 (2005)
Woerndl, W., Schulze, F., Yordanova, V.: Modeling and Learning Relevant Locations for a Mobile Semantic Desktop Application. Journal of Multimedia Processing and Technologies (JMPT) 1(1) (2010)
APML, http://apml.areyoupayingattention.com/ (accessed, May 2011)
CAMf, http://www.ariadne-eu.org/index.php?option=com_content&task=view&id=39&Itemid=55 (accessed, May 2011)
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Woerndl, W., Manhardt, A., Schulze, F., Prinz, V. (2011). Logging User Activities and Sensor Data on Mobile Devices. In: Atzmueller, M., Hotho, A., Strohmaier, M., Chin, A. (eds) Analysis of Social Media and Ubiquitous Data. MUSE MSM 2010 2010. Lecture Notes in Computer Science(), vol 6904. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23599-3_1
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DOI: https://doi.org/10.1007/978-3-642-23599-3_1
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