SP Seminar on Spatiotemporal Event Detection in Mobility Network on 11/30/2011
#Signal
#Processing
#Chapter
#of
#IEEE
#North
#Jersey
#Section
#Seminar
#on
#Spatiotemporal
#Event
#Detection
#in
#Mobility
#Network
#By
#Dr.
#Rong
#Duan
#Time:
#Nov
#30
#(Wednesday)
#4:00
#to
#5:00PM
#Place:
#Babbio
#Center
#310
#Stevens
#Institute
#Technology
#Hoboken
#NJ
#07030
#Information:
#Prof.
#Hong
#Man
#(201)
#216-5038
#hman@stevens.edu
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.
Bio:
Rong Duan received her M.S. degree in Computer Science and Ph.D degree in Computer Engineering respectively from Stevens Institute of Technology. She is currently Principle Member of Technical Staff at the AT&T labs, research in Florham Park, NJ . Her research interests include data mining, knowledge discovery, business applications with temporal and spatial data analysis, data integration and data quality assessment.
Her publications are in the areas of time series modeling, spatial-temporal even detection, and data quality control. Rong served as Secretary/Treasurer, Vice Chair and Chair for the Data Mining Section of INFORMS in 2006-2008, 2008-2009, 2009-2010 respectively. PC member of IEEE ICME and editorial review board member of International Journal of Information Systems in the Service Sector.
Time: Nov 30 (Wednesday) 4:00 to 5:00PM
Place: Babbio Center 310, Stevens Institute of Technology, Hoboken NJ 07030
Information: Prof. Hong Man (201) 216-5038, hman@stevens.edu
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- Babbio Center 310, Stevens Institute of Technology
- Hoboken, New Jersey
- United States
- Building: Babbio Center 310, Stevens Institute of Technology, NJ 07030
- Contact Event Host
- Prof. Hong Man (201) 216-5038, hman@stevens.edu
Agenda
Place: Babbio Center 310, Stevens Institute of Technology, Hoboken NJ 07030
Information: Prof. Hong Man (201) 216-5038, hman@stevens.edu