momie research overview
DESCRIPTION
Seminar presentation on April 12th 2012.MoMIE seminar on Mobile Usage Measurement, Analytics, and ResearchTRANSCRIPT
MoMIE Seminar
Dipoli, April 12th, 2012
Seminar program
MoMIE research overview
Timo Smura Dipoli, April 12, 2012
Outline
• MoMIE project overview
• Mobile usage measurements, 2005 – 2012
– Mobile network measurements
• Devices: Installed base, sales, and features
• Network traffic measurements
– Handset-based measurements
– Web analytics
• Holistic view of mobile usage
• Conclusions
MoMIE - Modeling of Mobile Internet Ecosystem
• Two-year research project funded by Tekes and industry partners
– Continues a series of projects since 2004
• Purpose:
– Measure, analyze, and model the mobile Internet ecosystem in Finland
– Understand and exploit quantitative usage data collected by the market parties
• Focus:
– Finnish consumer mobile market
– Mobile Internet services and applications
• http://momie.comnet.aalto.fi
Multi-method measurements, 2005-2012
Source: Modified from Kivi, 2011
MoMIE methods:
• Surveys on handset panel
• Handset monitoring
• Mobile operator
accounting systems
• IP traffic measurements
• Web analytics
Mobile network measurements
Network traffic measurements Measurement setup
• Annually one week measurement at mobile operators’ networks
• IP traffic measurement at Internet access point (1)
– Complemented with separate session logs (2)
• Variables detected include, e.g.: Devices, Applications, Content type,
Sessions
• Used for modelling and forecasting mobile Internet usage patterns
.UTRAN
Access Network
Gb
PS Core Network
SGSN GGSN
Internet
...
.
.Gn GiIuPS
GERAN
2) Session log
1) IP Traffic Measurement
Handset-based measurements
Handset-based measurements Research 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)
– 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 measurements Current 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 phonebooks
2. 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
3 3 2
53 47
66
12
12
8 8
9
7
24 29
17
Share ofinteractiontime (%)
Share ofsessions
(%)
Share oftotal timespent (%)
Shares of time and usage per context
Elsewhere
Other meaningful
Office
Home
Abroad
Sources: Karikoski & Soikkeli
Server-based measurements /
Web analytics
Server-based measurements / Web analytics
• Scope: Sanoma’s digital content – Normal web sites (e.g. www.hs.fi)
– Mobile-optimised sites (e.g. m.hs.fi)
– Native applications for smartphones (iOS, Android, Symbian^3)
• Key dimensions: – User Agent >> Identify mobile device models
• Vendors, operating systems, models
• Mapping to features
– IP Address >> Identify mobile networks
– Hour of day
• Key metrics: – Pageview, Visitor, Session
– Pageview duration, Session duration
Holistic view of mobile usage
Framework for analysing mobile usage Measurement points vs. service components
Modified from: Smura, Kivi, Töyli 2009
1: Network traffic measurements
2: Handset-based measurements
3: Web analytics
Conclusions
• MoMIE project 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
– 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
Contacts
• Project management: – Heikki Hämmäinen, Timo Smura
• Researchers: – Handset-based measurements
• Juuso Karikoski, Tapio Soikkeli
– Network measurements • Antti Riikonen
– Handset features and evolution • Timo Smura, Antti Riikonen
– Web analytics • Timo Smura
– Bayesian Belief Networks –based analytics • Pekka Kekolahti
• http://momie.comnet.aalto.fi