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This final report discusses the research on big data processing, analytics, and applications in mobile cellular networks. The team members from multiple countries and institutions worked together to address various challenges in this field, such as lack of benchmark studies, parallelization and distributed processing issues, and the need for timely delivery of information. The report presents the results of their work, including knowledge discovery, predictive models, graph analytics, and machine learning techniques applied to telecom data. The report also highlights the importance of understanding the spatio-temporal context of the data and the fusion of external data sources. Furthermore, it explores the applications of telecom data in various domains such as transportation, urban planning, public health, economy, and tourism. The report concludes with future work plans, including negotiation for new datasets, dissemination of results, and project proposals for continued activities.
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CS8 - Big Data Processing, Analytics and Applications in Mobile Cellular Networks Title cHiPSet – CS8Final Report Sanja Brdar Vilnius, March28-29, 2019
Team members Team Profile Countries: Serbia, Greece, Romania, Ireland, Austria, Norway Partners: 1 Research Institute, 5 Universities, 1 Company Team members Sanja Brdar Olivera Novović NastasijaGrujić Apostolos Papadopoulos Ciprian-Octavian Truică KenthEngø-Monsen Horacio Gonzalez-Velez Siegfried Benkner Enes Bajrović
Workflow MobileCellular Networks - From Location Data to Applications
Telcom data Call Detail Records Data from 3 cities: • Milan, Trentino (Italy), Telecom Italia • Novi Sad (Serbia), Telecom Serbia Activity, Connectivity, Mobility…
Big Data Processing Real-time settings raise critical issues! Challenges: Lack of research and benchmark studies that evaluate different computational architectures and big data frameworks Only a few studies tackled issues of parallelization and distributed processing Timely dilevery of information
Data Analytics Knowledge discovery and predictive models Graph analytics and machine learning - indispensable tools for telecom data analytics Challenges: streaming nature of data demands for change detection and online adaption
Fusion with other sources Understand spatio-temporal context better Challenges: mismatch in the resolutions, multimodal and dynamic nature of data External data sources are also advancing (new satellites launched, enhanced IoT ecosystems…)
Applications Better decision making in the domains of application Telecomdatasignificantly enriched many different fields and external social good applications: transportation, urban and energy planning, public health, economy, tourism...
CS work - community detection Apache Spark, Apache Hive and programming language Scala fully exploited in new pipeline Results presented at the 20th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK, September 2018
CSwork- detection of frequent patterns Detection of frequent patterns in community structure for selected location External sources: POI and land cover maps FP-Growth algorithm Scala implementation in Apache Spark’s Mllib
CS work- Visualisations Automatization of visualisation pieline using Python QGIS API
CS work- Social pulse City during large summer festival – Exit festival Novi Sad, Serbia
CS work- Social pulse Mobilities trajectories, location entropy, agent based modelling
Future work • Negotiation for a new dataset from another telco provider in Serbia • Next dissemination of results: NetMob 2019, 8-10 July Mathematical Institute, Oxford University, Oxford • Project proposals to continue activities • Bilateral • H2020 – Marie Skłodowska-Curie Actions • (RISE, ITN), RIA, IA • Eureka
