Ville Könönen gave a presentation at the Smart Interaction in Mobile and Media customer seminar 24.9.2013.

1. VTT intelligent data analytics
How to utilize intelligently sensor information in services – two
examples
Smart Interaction in Mobile and Media
Ville Könönen
VTT Technical Research Centre of Finland

2. 26/09/2013
Data analytics in bigdata
Data analytics together with bigdata
has a potential to create big
advantages for service providers
In traditional service markets
such as
Telecom operators
Retail
Security
New market roles enabling digital
value chains such as
Data brokering
Real time data processing
2

3. 26/09/2013
VTT data analytics
Data analysis – statistical data
analysis methods, descriptive
(clustering, etc.) and predictive
(regression, neural networks,
etc.).
HW and SW solutions need for
data collection and
management
Analytics – several teams
working in different application
areas: telecom, logistics,
business, etc.
3

4. 26/09/2013
Example 1:
AUTOMATIC MEDIA EXPOSURE TRACKING
4

5. 26/09/2013
5
Outline
Aims
Companies want to gather information of user
behavior and user motivations - costeffectively
Information is needed in order to produce
timely media and advertising content in right
context
From silo- and media-centered measuring to
holistic consumer-centric information (as
media field is becoming more and more
fragmented and multichannel)
Goal is to collect raw data for preserving as much information as
possible. Suitable backends can be provided for different customers

6. 26/09/2013
Software architecture
The software consists of two parts:
Background service recording information in background
Client for inputting information that cannot be
detected/recorded automatically, e.g. reading newspaper
In addition, there is a related web service that can be used to
manually annotate a media day for evaluation of the automatic
system
URL: http://wizard.erve.vtt.fi
6

7. 26/09/2013
Background service
Background service collects the following information:
User ID
Application information (app name, app class, timing)
Browser events (url, timing).
Device info (Android version, device class, product name)
Hard button events (volume up/down, event timing)
Screen touch events (x,y,timing information, screen
orientation)
Periodic information (device location, data usage, timing)
The information is sent to an external server once per day
7

8. 26/09/2013
8
Example of additional processing
Start
Stop
Type
Brand
Topic
Location
Context
12:30:12
12:32:10
Newspaper
Helsingin
sanomat
Economy
Lat: 60.27
Long: 24.98
Work
19:10:01
20:10:31
Net TV
YLE Areena
Docventure
s
Lat: 61.48
Long: 21.79
Home
Start
Stop
Application
Application
type
Location
Context
9:01:22
9:15:00
Facebook
SOME app
Lat: 60.27
Long: 24.98
Work
10:00:12
10:00:32
Calendar
System app
Lat: 60.27
Long: 24.98
Work

9. 26/09/2013
9
Example 2:
AUTOMATIC BEHAVIORAL MODEL BASED TESTING
OF SMART PHONES AND APPLICATIONS

10. 26/09/2013
Outline
Testing smart phones and applications
is a complex task
Model based testing is possible but
exhaustive testing of all the functionality
is often not possible due to time
constraints
A solution is to learn behavioral models
by observing real usage patterns of
device users
Statistical models are used to model
user behavior
10

11. 26/09/2013
11
Schematic view
1. Behavioral data is collected from
Behavior
recording apps
smart phones
UI sequences
Rich context info
2. Statistical server side model learning
3. Test robot control
--- OR --3. Stimulator software such as
MonkeyRunner
Statistical model
learning
Test robot control
Monkey
Runner

12. 26/09/2013
VTT creates business from technology
12