Mobility Genome™- A Framework for Mobility Intelligence from Large-Scale Spatio-Temporal Data

Deepak, Jayakumaran; Wang, Jingxuan; Luo, Shixin; Jin, Yunye; Ng, Yibin; Lim, Aloysius; Liu, Ying

doi:10.1109/dsaa.2017.20

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Mobile phone data can reveal the approximate location of a user and its mobility trace based on geographical location of the Radio Base Stations which registered the traffic. In [16] the authors proposed a novel computational framework that enables efficient and extensible discovery of mobility intelligence from largescale spatial-temporal data such as CDR, GPS and Location Based Services data. In [25] the authors focus on usage of Call Detail Records (CDR) in the context of mobility, transport and transport infrastructure analysis.…”

Section: Mobilitymentioning

confidence: 99%

“…Only a few studies tackled issues of parallelization and distributed processing. In [16] authors proposed mobility intelligence framework based on Apache Spark for processing and analytics of large scale mobile phone data. Another example is the study [58] that provided computational pipeline for the community detection in mobile phone data, developed in Apache Hive and Spark technology, and benchmarked different architectures and settings.…”

Section: Summary and Visionmentioning

confidence: 99%

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Big Data Processing, Analysis and Applications in Mobile Cellular Networks

Brdar

Novovic

Grujić

et al. 2019

Lecture Notes in Computer Science

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When coupled with spatio-temporal context, location-based data collected in mobile cellular networks provide insights into patterns of human activity, interactions, and mobility. Whilst uncovered patterns have immense potential for improving services of telecom providers as well as for external applications related to social wellbeing, its inherent massive volume make such 'Big Data' sets complex to process. A significant number of studies involving such mobile phone data have been presented, but there still remain numerous open challenges to reach technology readiness. They include efficient access in privacy-preserving manner, high performance computing environments, scalable data analytics, innovative data fusion with other sources-all finally linked into the applications ready for operational mode. In this chapter, we provide a broad overview of the entire workflow from raw data access to the final applications and point out the critical challenges in each step that need to be addressed to unlock the value of data generated by mobile cellular networks.

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Section: Mobilitymentioning

confidence: 99%

Section: Summary and Visionmentioning

confidence: 99%