Analysis of mobile phone data under a cloud computing framework

With the increasing amount of information, the percentage of processed large-scale heterogeneous data is rapidly growing. Take mobile phones as an example. Due to their various sensors, high presence among people, and cost-effectiveness, they are becoming very popular. This has made it easy to obtain users' contextual data whose collection can be valuable in engineering and business domains, e.g., transportation, location-based services, and advertisement industry. The data volume generated by mobile phones and the need to analyze the information near the real-time are the challenges facing researchers. Recently, big data has attracted much attention from academia, industry as well as government. There is a strong need for new technologies, e.g., frameworks based on cloud computing, that could not only process and analyze a large volume of data, but also ingest a large volume of data at a fast pace. In this paper, we propose to use a dynamic data analysis framework to explore and analyze mobile phone data. After preprocessing the dataset consisting of cell phone communication records, this paper presents an interactive exploratory spatial data analysis algorithm and its analysis results.

[1]  MengChu Zhou,et al.  Estimating travel speed via sparse vehicular crowdsensing data , 2016, 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT).

[2]  Iis P. Tussyadiah.  A Concept of Location-Based Social Network Marketing , 2012 .

[3]  Shashi Shekhar,et al.  Spatial Databases: A Tour , 2003 .

[4]  Douglas M. Hawkins.  Identification of Outliers , 1980, Monographs on Applied Probability and Statistics.

[5]  MengChu Zhou,et al.  Analyzing the evolution of rare events via social media data and k-means clustering algorithm , 2016, 2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC).

[6]  Yorgos N. Photis,et al.  Spatial analysis of the electrical energy demand in Greece , 2017 .

[7]  Cai Qi,et al.  CORRIGENDUM: Analysis of interactions between SNARE proteins using imaging ellipsometer coupled with microfluidic array , 2014, Scientific Reports.

[8]  Peter Mell,et al.  "The NIST Definition of Cloud Computing," Version 15 , 2009 .

[9]  Bu-Sung Lee,et al.  DynamicMR: A Dynamic Slot Allocation Optimization Framework for MapReduce Clusters , 2014, IEEE Transactions on Cloud Computing.

[10]  Bingsheng He,et al.  MrPhi: An Optimized MapReduce Framework on Intel Xeon Phi Coprocessors , 2015, IEEE Transactions on Parallel and Distributed Systems.

[11]  Crispin Cooper,et al.  Using spatial network analysis to model pedal cycle flows, risk and mode choice , 2017 .

[12]  T. Du,et al.  Building a targeted mobile advertising system for location-based services , 2012, Decis. Support Syst..

[13]  MengChu Zhou,et al.  Predicting Stay Time of Mobile Users With Contextual Information , 2013, IEEE Transactions on Automation Science and Engineering.

[14]  Betul Karakus,et al.  Architecture and Implementation of a Scalable Sensor Data Storage and Analysis System Using Cloud Computing and Big Data Technologies , 2015, J. Sensors.

[15]  MengChu Zhou,et al.  Understanding the evolution of a disaster—a Framework for Assessing Crisis in a System Environment (FACSE) , 2012, Natural Hazards.

[16]  Pu Wang,et al.  Development of origin–destination matrices using mobile phone call data , 2014 .

[17]  Kari Laasonen,et al.  Clustering and Prediction of Mobile User Routes from Cellular Data , 2005, PKDD.

[18]  María Amparo Núñez Andrés,et al.  Towards a collective spatial analysis: Proposal of a new paradigm for supporting the spatial decision-making from a Geoprospective approach , 2015, 2015 1st International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM).

[19]  Marin Litoiu,et al.  A Data Platform for the Highway Traffic Data , 2014, 2014 IEEE 8th International Symposium on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems.

[20]  Lu Wang,et al.  Evolution trends of the network structure of Spring Airlines in China: A temporal and spatial analysis , 2017 .

[21]  MengChu Zhou,et al.  An Incremental and Distributed Inference Method for Large-Scale Ontologies Based on MapReduce Paradigm , 2015, IEEE Transactions on Cybernetics.

[22]  Monidipa Das,et al.  A cost-efficient approach for measuring Moran's index of spatial autocorrelation in geostationary satellite data , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[23]  MengChu Zhou,et al.  Mobility-Aware Service Composition in Mobile Communities , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[24]  Sally A. McKee,et al.  Understanding the behavior of in-memory computing workloads , 2014, 2014 IEEE International Symposium on Workload Characterization (IISWC).

[25]  M. Barthelemy,et al.  From mobile phone data to the spatial structure of cities , 2014, Scientific Reports.

[26]  MengChu Zhou,et al.  Toward cloud computing QoS architecture: analysis of cloud systems and cloud services , 2017, IEEE/CAA Journal of Automatica Sinica.

[27]  Marta C. González,et al.  Origin-destination trips by purpose and time of day inferred from mobile phone data , 2015 .

[28]  Martin Raubal,et al.  Correlating mobile phone usage and travel behavior - A case study of Harbin, China , 2012, Comput. Environ. Urban Syst..

[29]  Mohammad Al Hasan,et al.  An Iterative MapReduce Based Frequent Subgraph Mining Algorithm , 2013, IEEE Transactions on Knowledge and Data Engineering.

[30]  Athanasios V. Vasilakos,et al.  Parallel Processing Systems for Big Data: A Survey , 2016, Proceedings of the IEEE.

[31]  MengChu Zhou,et al.  Improved Rule Installation for Real-Time Query Service in Software-Defined Internet of Vehicles , 2017, IEEE Transactions on Intelligent Transportation Systems.