## 1 Introduction  
Nowadays, there are more mobile phones on this planet than humans. A mobile phone is a necessity in most of our society, and we usually carry it with us not only during the day, but also, in many cases, even at night. In this context, the exponential growth of human behavioural data is related with the use of mobile phones. Thus, an analysis about how someone uses the mobile phone can give us useful information about his/her behaviour. This information can have a great significance if it is applied in many real applications, such as service recommendations or urban planning. However, because of the very large amounts of data that are created by mobile phone users, the treatment of these data is usually complex. In addition, and considering the final goal to achieve, sometimes only part of this extracted data is relevant to analyse. Especially, in the competitive telecommunications market, the stored call data are essential to maintain and manage their customers. The approach proposed in this paper analyses only the stored call logs to extract information about different human behaviours. The main reason for this is that, as far as we know, not much emphasis has been given to extract dynamic human behaviours in real-time from the analysis of call detail records (CDR) as data streams. In relation with the study of human behaviours, the first research was proposed in the 19 th century [1]. From then, many different techniques have been applied to study several aspects of human behaviour. Many recent papers have studied how cellular network data can be used for urban planning. In [2], the authors propose the use of CDR data to analyse people flow in and out of a suburban city. That CDR data analysis can help urban planners better understand city dynamics. In a different study [3], it is demonstrated that the intensity of urban activities and their evolutions through space and time can be represented using call volume and cellular towers. In relation with the service recommendation area, a complete statistical analysis on the mobile users' data is proposed in [4]. In that research, the key attributes that are significant for a phone changing event are recognized, and four classifiers to predict whether people change their phones are proposed. Finally, in relation to public health studies, recent studies have demonstrated the potential application of smartphone apps in patient self-monitoring [5]. In addition, a recent study [6] aims to identify and analyse cancer-related applications available on the Apple iTunes platform. This paper is focussed on the analysis of human behaviours or patterns in relation to the use of their mobile phone by using only data from CDR. Thus, the proposed approach automatically extracts and analyses different calling behaviours. In addition, the information from the mobile phones is usually obtained in real-time so it is necessary to analyse that information by using techniques that can work in real-time. However, in the addressed environment, the behaviour patterns change due to the dynamic nature of human activities and these changes need to be considered. In this sense, scalable algorithms to identify frequent human behavioural patterns using a variety of phone sensors (location, WiFi, and so on) are proposed in [7]. In that research, its scalability is a salient advantage. However, we propose a method which can analyse CDR data in real-time with no need to store the information about the calls analysed. This aspect is important not only because the algorithm implementation is more efficient, but also because it supports the privacy of customers. Thus, the main novelty of this approach is that all the CDR are read as data streams but they do not need to be stored--the results are obtained in real-time in a recursive manner. The real-time behaviour of the algorithms which work in this recursive manner have been tested already [8]. For this reason, this approach can be used in real-time with a very large number of call records.