Mining Groups in Mobile Monitoring Log

On Mining Mobile Users by Monitoring Logs

Dmitry Namiot

Lomonosov Moscow State University

i-ASC 2014

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?


Agenda

• Passive monitoring for mobile users• Web Log analogue • Missed records and the specifics for mobile statistics• Related works• Group visits


Passive monitoring • source address (MAC-address)• SSID• supported rates• additional request information• extended support rates• vendor specific information


Passive monitoring

• Wi-Fi router• Detects Wi-Fi

(Bluetooth) devices• External database

(MySQL)• 70% detection rate


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


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


Related works


Related works


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?


Clusters

Increased interval Increased frequency


Groups mining

• find clusters for the each day

• detect the sequences of clusters across all days with some minimum set of common members


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


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..

Technology

Mining Groups in Mobile Monitoring Log