IAC 2024 - IA Fast Track to Search Focused AI Solutions
Mining Groups in Mobile Monitoring Log
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?
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
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.