Impact of CDR data analysis using big data technologies for the public: An analysis

A Call Data Record (CDR) is an information structure storing applicable information about a given telephonic activity including a customer of a telephonic framework. A CDR, as a rule, contains spatial and temporal data and it can convey other extra helpful information. The primary motivation of this survey is to investigate the current procedures in call data records so that the specialists can incorporate all the fundamental measurements in their works in this domain and the limitations of the current ones can be overcome. In this survey, assorted issues incorporated into call data record strategies is recognized and distinctive call data record systems are studied to find which attributes is tended to in a given and which is overlooked. To categorize the strategies, all articles that had "call data records" in its title or as its keyword published between 2004 to 2016, is initially chosen from the scientific journals: IEEE, Elsevier, Springer and international journals. Furthermore, this study gives a detailed idea regarding call data record.

[1]  M. Khan,et al.  Automatic Monitoring & Detection System (AMDS) for Grey Traffic , 2015 .

[2]  Vincent D. Blondel,et al.  A survey of results on mobile phone datasets analysis , 2015, EPJ Data Science.

[3]  Jonathan Cinnamon,et al.  Evidence and future potential of mobile phone data for disease disaster management , 2016, Geoforum.

[4]  Eric Fleury,et al.  Call detail records to characterize usages and mobility events of phone users , 2016, Comput. Commun..

[5]  O. Järv,et al.  Understanding monthly variability in human activity spaces: A twelve-month study using mobile phone call detail records , 2014 .

[6]  Gennaro Boggia,et al.  Modeling of Call Dropping in Well-Established Cellular Networks , 2007, EURASIP J. Wirel. Commun. Netw..

[7]  Corrado Moiso,et al.  Identifying user habits through data mining on call data records , 2016, Eng. Appl. Artif. Intell..

[8]  Hossam Afifi,et al.  Large scale model for information dissemination with device to device communication using call details records , 2015, Comput. Commun..

[9]  Chi-Chun Lo,et al.  Traffic speed estimation based on normal location updates and call arrivals from cellular networks , 2013, Simul. Model. Pract. Theory.

[10]  Engin Zeydan,et al.  Anomaly Detection In Cellular Network Data Using Big Data Analytics , 2014 .

[11]  Paola Pellegrini,et al.  A detailed analysis of the actual impact of real-time railway traffic management optimization , 2016, J. Rail Transp. Plan. Manag..

[12]  Vanessa Frías-Martínez,et al.  A Gender-Centric Analysis of Calling Behavior in a Developing Economy Using Call Detail Records , 2010, AAAI Spring Symposium: Artificial Intelligence for Development.

[13]  David Taylor,et al.  Interactions of natural hazards and society in Austral-Asia: evidence in past and recent records , 2004 .

[14]  Marco Luca Sbodio,et al.  AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data , 2013, ECML/PKDD.

[15]  Ngoc Thanh Nguyen,et al.  Constructing and mining a semantic-based academic social network , 2010, J. Intell. Fuzzy Syst..

[16]  Seungwoo Jeon,et al.  Monte Carlo simulation-based traffic speed forecasting using historical big data , 2016, Future Gener. Comput. Syst..

[17]  Rupesh K. Gopal,et al.  A Rule-based Approach for Anomaly Detection in Subscriber Usage Pattern , 2007 .

[18]  Ricky K. Taira,et al.  A neuro-oncology workstation for structuring, modeling, and visualizing patient records , 2010, IHI.

[19]  Helmut Hlavacs,et al.  The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring , 2015, IEEE Transactions on Intelligent Transportation Systems.

[20]  Ricky K. Taira,et al.  Context-Based Electronic Health Record: Toward Patient Specific Healthcare , 2012, IEEE Transactions on Information Technology in Biomedicine.

[21]  Lalit chettri,et al.  RF Optimization for call setup and analysis of GSM network using agilent tools , 2016 .

[22]  Dandan Yin,et al.  Computing on Base Station Behavior Using Erlang Measurement and Call Detail Record , 2015, IEEE Transactions on Emerging Topics in Computing.

[23]  Jari Saramäki,et al.  Estimation and monitoring of city-to-city travel times using call detail records , 2016, EPJ Data Science.

[24]  Basabi Chakraborty,et al.  A review on application of data mining techniques to combat natural disasters , 2016, Ain Shams Engineering Journal.

[25]  Lipika Dey,et al.  Anomaly Detection from Call Data Records , 2009, PReMI.

[26]  Chiara Renso,et al.  Analysis of GSM calls data for understanding user mobility behavior , 2013, 2013 IEEE International Conference on Big Data.