03604nas a2200241   4500008004100000022001400041245004300055210004200098300001700140490000600157520297600163653001303139653001403152653001603166653002103182653001503203653001903218100002803237700001503265700001703280700001803297856004703315       2011                        eng d  a1556-466500aBehavior-based Adaptive Call Predictor0 aBehaviorbased Adaptive Call Predictor  a21:1–21:280 v63 a<p>Predicting future calls can be the next advanced feature of the next-generation telecommunication networks as the service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedules, avoiding unwanted communications (e.g. voice spam), and resource planning in call centers. Predicting calls is a very challenging task. We believe that this is an emerging area of research in ambient intelligence where the electronic devices are sensitive and responsive to people’s needs and behavior. In particular, we believe that the results of this research will lead to higher productivity and quality of life. In this article, we present a Call Predictor (CP) that offers two new advanced features for the next-generation phones namely “Incoming Call Forecast” and “Intelligent Address Book.” For the Incoming Call Forecast, the CP makes the next-24-hour incoming call prediction based on recent caller’s behavior and reciprocity. For the Intelligent Address Book, the CP generates a list of most likely contacts/numbers to be dialed at any given time based on the user’s behavior and reciprocity. The CP consists of two major components: Probability Estimator (PE) and Trend Detector (TD). The PE computes the probability of receiving/initiating a call based on the caller/user’s calling behavior and reciprocity. We show that the recent trend of the caller/user’s calling pattern has higher correlation to the future pattern than the pattern derived from the entire historical data. The TD detects the recent trend of the caller/user’s calling pattern and computes the adequacy of historical data in terms of reversed time (time that runs towards the past) based on a trace distance. The recent behavior detection mechanism allows CP to adapt its computation in response to the new calling behaviors. Therefore, CP is adaptive to the recent behavior. For our analysis, we use the real-life call logs of 94 mobile phone users over nine months, which were collected by the Reality Mining Project group at MIT. The performance of the CP is validated for two months based on seven months of training data. The experimental results show that the CP performs reasonably well as an incoming call predictor (Incoming Call Forecast) with false positive rate of 8%, false negative rate of 1%, and error rate of 9%, and as an outgoing call predictor (Intelligent Address Book) with the accuracy of 70% when the list has five entries. The functionality of the CP can be useful in assisting its user in carrying out everyday life activities such as scheduling daily plans by using the Incoming Call Forecast, and saving time from searching for the phone number in a typically lengthy contact book by using the Intelligent Address Book. Furthermore, we describe other useful applications of CP besides its own aforementioned features including Call Firewall and Call Reminder.</p>
10abehavior10acall logs10acall matrix10aconvergence time10aPrediction10atrace distance1 aPhithakkitnukoon, Santi1 aDantu, Ram1 aClaxton, Rob1 aEagle, Nathan  uhttp://doi.acm.org/10.1145/2019583.201958802097nas a2200217   4500008004100000020002200041245005200063210005200115260003500167520147400202653001001676653001501686653002201701653001801723653002001741653002501761653002401786100001701810700001501827856003701842       2010                        eng d  a978-1-4244-6444-900aPredicting social ties in mobile phone networks0 aPredicting social ties in mobile phone networks  aVancouver, BC, Canadac05/20103 a<p>A social network dynamically changes since the social relationships (social ties) change over time. The evolution of a social network mainly depends on the evolution of the social relationships. The social-tie strengths of person-to-person are different one another even though they are in the same group. In this paper we investigate the evolution of person-to-person social relationships, quantify and predict social tie strengths based on call-detail records of mobile phones. We propose an affinity model for quantifying social-tie strengths in which a reciprocity index is integrated to measure the level of reciprocity between users and their communication partners. Since human social relationships change over time, we map the call-log data to time series of the social-tie strengths by the affinity model. Then we use ARIMA model to predict social-tie strengths. For validation of our results, we used actual call logs of 81 users collected for a period of 8 months at MIT by the Reality Mining Project group and also used call logs of 20 users collected for a period of 6 months by UNT's Network Security team. These users have around 5000 communication partners. The experimental results show that our model is effective. We achieve prediction performance with accuracy of average 95.2% for socially close and near members. Among other applications, this work is useful for homeland security, detection of unwanted calls (e.g., spam), and marketing.</p>
10aARIMA10aPrediction10areciprocity index10asocial groups10aSocial networks10asocial relationships10aSocial-tie strength1 aZhang, Huiqi1 aDantu, Ram  uhttps://nsl.cse.unt.edu/node/141