Real-Time Event Detection System for Mobile Data

Abstract

Mobile data is one of the prior data sources which can be used for urban study analytics due to the amount of valuable information they contain, such as type of mobile data, time and most importantly data’s coordinates. In order to keep the cell services stable for users all across the country it is crucial for authorities to be aware of unannounced gatherings which can cause traffic overload on cell towers in the area. In this thesis implementation of a enterprise system has been demonstrated for monitoring the behavior of the cell towers under the administration’s authority. The core functionality of this system is detecting ongoing events in different areas on an hourly-basis schedule utilizing multiple statistical approaches for abnormality detection. The output of the event detection section of the system is an approximate estimation of the ongoing event’s location on the map. Current design of the system is aiming to fullfill the downsides of similar approaches for event and crowd detection such as high processing expenses and noncomprehensive resources by using parallel servers, distributing the processing load while keeping the pipline clear for user’s demands, and utilizing Call Detail Records (CDR) data as input resources which gives the advatages of containing the majority of mobile transactions and human behavior in the city.