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Context detection and effects on behaviorElisa workshop on “Lifestyle Sensing and Behavioral Analytics”, June 29th, 2012
Dr. Timo Smura, Dep. of Communications and Networking(presented work by T. Soikkeli, J. Karikoski, H.-H. Jo, M. Karsai, et al.)
Outline
• Behavioral data collection in Aalto / Comnet– Multi-point measurements– Examples of data sets– Holistic view of service usage
• Ongoing work related to contexts and behavior– Handset based measurements– Location detection– Context detection algorithms– Context dependence of application and service usage
Behavioral data collection
Multi-point measurementsPotential sources of digital behavioral data
Our data sources:
• Handset monitoring panels + questionnaires
• IP traffic measurements• Web analytics systems• Mobile operator
accounting systems
Holistic view of service usageMeasurement points vs. service components
Modified from: Smura, Kivi, Töyli 2009
Context detection andeffects on behavior
Handset-based measurementsResearch process and data
• Based on a software client installed to a panel of smartphones• Collects rich data about handset usage:
– What: Application, bearer– Where: Base station cell IDs (hashed), WLAN SSID– When: Time stamps– How much: Time stamps, amount of generated traffic
• Gives a detailed view of the usage patterns and behavior of panelists– All applications, also offline and WLAN usage– Location / context detection
Source: Karikoski 2012
Handset-based measurementsCurrent focus areas
1. Multi-channel communications services– Diversification of communications
channels (phone calls, SMS, email, social media services)
– Effect of relationship type on channel selection
– Mobile social phonebooks2. Location and context detection
– Context detection algorithms– Human behavior and time use in
different locations and contexts– Effects on usage: e.g., sessions,
applications / services
332
5347
66
1212
8 89
7
242917
Share ofinteractiontime (%)
Share ofsessions
(%)
Share oftotal timespent (%)
Shares of time andusage per context
Elsewhere
Other meaningful
Office
Home
Abroad
Sources: Karikoski & Soikkeli
Location detection based on cell ID
Source: Jo et al. 2012
Context detection algorithmsSimplified version, not utilizing WLAN SSID data
A) Temporal boundaries for user’s trajectory in cells:
B) Duration, i.e., time spent by user in cell c:
C) ”Abroad” context determined by Mobile Network Code (MNC)
D) For the cells in Finland, more detailed durations:
Sources: Soikkeli 2011, Jo et al. 2012
E) Criteria for assigning other contexts:
Application usage by contextExemplary data from a single user during two days
Source: Jo et al. 2012
Context dependence of service usageFractions and intensities of service usage by context
Source: Jo et al. 2012
Conclusions (1/2)
• Aalto / Comnet collects rich data on mobile usage– Continues a series of measurements since 2005– Holistic view of mobile devices and services in Finland
• Each measurement methods has its pros and cons– Level of: Granularity, Coverage, Representativity– In terms of: Devices, Applications, Networks, Content
• Actors have different views to mobile usage and users– E.g., Device vendors vs. Operators vs. Content providers– Increasing value of user data induces competition
• May lead to, e.g., traffic encryption, routing via own gateways
Conclusions (2/2)
• Data collected by current smartphones can be used to infer the context of people– Then use it as a variable to explain behavior
• By far, research has focused on developing and testing the technical algorithms for detecting the contexts– Demonstration of value with descriptive analysis of usage data
• Examples of statistical analyses on the effect of context on behavior are still rare– Typically based on survey-based studies and self-reported context
and usage information– Ongoing / future work: combine existing theories and hypothesis-
based statistical methods to the data collected in smartphone monitoring panels
References
• Soikkeli, T. (2011). The effect of context on smartphone usage sessions. M.Sc. Thesis.
• Karikoski, J., & Soikkeli, T. (In Press) Contextual usage patterns in smartphone communication services, Personal and Ubiquitous Computing.
• H.-H. Jo, M. Karsai, J. Karikoski, and K. Kaski, Spatiotemporal correlations of handset-based service usages, arXiv:1204.2169 (2012)
• Smura, T., Kivi, A., & Töyli, J. (2009). A Framework for Analysing the Usage of Mobile Services, info, vol. 11, no. 4, pp. 53-67.
Useful contacts in Aalto / Comnet
• Project management:– Prof. Heikki Hämmäinen, Timo Smura
• Researchers:– Handset-based measurements
• Juuso Karikoski, Tapio Soikkeli– Mobile network traffic measurements
• Antti Riikonen– Handset features and evolution
• Timo Smura, Antti Riikonen– Web analytics –based research
• Timo Smura– Bayesian Belief Networks –based analytics
• Pekka Kekolahti• [email protected]• http://momie.comnet.aalto.fi
