Presentation for i-ASC Workshop / ECIR - 2014

1. On Mining Mobile Users by
Monitoring Logs
Dmitry Namiot
Lomonosov Moscow State University
i-ASC 2014

2. Dmitry Namiot
http://servletsuite.blogspot.com
• Passive monitoring for mobile users lets us
anonymously collect presence information
about mobile visitors
• This information is linked to some predefined
place
• For any such place we can talk about some
visiting patterns
• How can we restore some of the patterns
from our monitoring log?
What are we talking about?

3. Dmitry Namiot
http://servletsuite.blogspot.com
Agenda
• Passive monitoring for mobile users
• Web Log analogue
• Missed records and the specifics for mobile
statistics
• Related works
• Group visits

4. Dmitry Namiot
http://servletsuite.blogspot.com
Passive monitoring
• source address (MAC-
address)
• SSID
• supported rates
• additional request
information
• extended support rates
• vendor specific
information

5. Dmitry Namiot
http://servletsuite.blogspot.com
Passive monitoring
• Wi-Fi router
• Detects Wi-Fi
(Bluetooth) devices
• External database
(MySQL)
• 70% detection rate

6. Dmitry Namiot
http://servletsuite.blogspot.com
Web Log
• Remote IP address – MAC address
• User-Agent header – parsed from MAC
• Missed URI field
• Missed Referrer field
• New field: SSID. PNL – preferred networks list

7. Dmitry Namiot
http://servletsuite.blogspot.com
Specifics
• Detection rate: 70%-80%
• It could not be predicted. Depends on mobile
OS, applications, etc.
• A reasonable assumption: the percentage for
missed records is about the same
• Use relative values instead of absolute figures.
E.g., trend in attendance versus visitors
counting
• Testing hypotheses about the results of
external influences

8. Dmitry Namiot
http://servletsuite.blogspot.com
Related works

9. Dmitry Namiot
http://servletsuite.blogspot.com
Related works

10. Dmitry Namiot
http://servletsuite.blogspot.com
Groups
• Group of friends,
which meets within a
certain time
• Not all of them are
present at each
meeting
• Not all of them arrive
simultaneously
• Can we discover such
groups?

11. Dmitry Namiot
http://servletsuite.blogspot.com
Clusters
Increased interval Increased frequency

12. Dmitry Namiot
http://servletsuite.blogspot.com
Groups mining
• find clusters for the
each day
• detect the sequences
of clusters across all
days with some
minimum set of
common members

13. Dmitry Namiot
http://servletsuite.blogspot.com
Conclusion
• A new model for mining mobile monitoring log
• Business-oriented reports about mobile groups
• Tested on real example (café in office building, 8
groups from 11)
• Applied areas: Smart Cities applications, retail

14. Dmitry Namiot
http://servletsuite.blogspot.com
OIT Lab
• Faculty of Computational Mathematics and
Cybernetics, Lomonosov Moscow State
University. Research areas are:
• telecom and software services, open API
for telecom, Smart Cities, M2M applications,
context-aware computing.