An Efficient Protocol for Predicting User Behaviour Using Call Detail Data

Wireless Mobile devices has led to the explosion of the computing world. This huge information can be utilized by observing the mobile network’s behavior. The big data thus generated in the mobile networks enable us to gain useful insights into the user patterns by using big data analysis techniques and computing techniques. The proposed model introduces uses data set information, data analysis techniques to categorize the data set in the network communication into two types, one is user based and the other one is network oriented. The essential data is then computed into by the telecommunication operators who are facing the tremendous challenge to provide satisfactory service to mobile users with varying QoS requirement. Thus by including high volume media transmission, huge amount of machine to machine(M2M) connectivity the data is summarized and reviewed to form temporal and spatial analysis to data mining and statistical test. The call measurement and call detail record, respectively, to understand the base station behavior. The users behavior is revealed and predicted by comparing the base stations location and real-world map and determine the behavior of the users.