TITLE: Spatiotemporal Event detection in Mobility Network

SPEAKER: Rong Duan

ABSTRACT:

Learning and identifying events in network traffic is crucial for service
providers to improve their mobility network performance. In fact, large
special events attract cell phone users to relative small areas, which
causes sudden surge in network traffic. To handle such increased load,
it is necessary to measure the increased network traffic and quantify
the impact of the events, so that relevant resources can be optimized
to enhance the network capability. However, this problem
is challenging due to several issues: (1) Multiple periodic temporal
traffic patterns (i.e., nonhomogeneous process) even for normal traffic;
(2) Irregularly distributed spatial neighbor information;
(3) Different temporal patterns driven by different events even for
spatial neighborhoods; (4) Large scale data set.

This paper proposes a systematic event detection method that deals
with the above problems. With the additivity property of Poisson process,
we propose an algorithm to integrate spatial information by aggregating
the behavior of temporal data under various areas. Markov Modulated
Nonhomogeneous Poisson Process (MMNHPP) is employed to estimate the
probability with which event happens, when and where the events take
place, and assess the spatial and temporal impacts of the events.
Localized events are then ranked globally for prioritizing more
significant events. Synthetic data are generated to illustrate our
procedure and validate the performance. An industrial example from a
telecommunication company is also presented to
show the effectiveness of the proposed method.

Contact: rongduan@research.att.com