momie research overview

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

• firstname.lastname@aalto.fi

• http://momie.comnet.aalto.fi