Why BMDA

Nowadays, we have the means to collect, store and process mobility data of an unprecedented quantity, quality and timeliness. This is mainly due to the wide spread of GPS-equipped devices, including new generation smartphones. As ubiquitous computing pervades our society, mobility represents a very useful source of information. Movement traces left behind, especially when combined with societal data, can aid transportation engineers, urban planners, and eco-scientists towards decision making in a wide spectrum of applications, such as traffic engineering and risk management. However, when these mobility data come in huge amounts, heterogeneous formats and possibly with privacy issues, novel methods have to be studied for proper analysis tasks. 


The International Workshop on Big Mobility Data Management (BMDA) is a series of workshops held since 2018 in conjunction with the Extending Data Base Technologies conference. The objective is to bring together researchers and practitioners interested in solutions for scalable data-intensive applications that manage and analyze big mobility data. BMDA themes include: 


BMDA editions:

* BMDA 2024 with EDBT 2024 – March 2024 - Paestum, Italy 

* BMDA 2023 with EDBT 2023, March 23, 2023, Ioannina, Greece 

* BMDA 2022 with EDBT 2022, March 29, 2022, Edinburgh, UK 

* BMDA 2021 with EDBT 2021, March 23, 2021, Nicosia, Cyprus (virtual) 

* BMDA 2020 with EDBT 2020, March 30, 2020, Copenhagen, Denmark (virtual) 

* BMDA 2019 with EDBT 2019, March 26, 2019, Lisbon, Portugal 

* BMDA 2018 with EDBT 2018, March 26, 2018, Vienna, Austria 


BMDA proceedings are published at CSUR and are therefore open access. So far (2021 included), we count *28* published research papers and demos. 

Best papers are invited for submission to a Geoinformatica BMDA special issue. So far, *7* papers, long versions of workshop papers, have been published at Geoinformatica: 

BMDA'2024 SI: in progress

BMDA'2023 SI (link):

  • Mandalis, P., Chondrodima, E., Kontoulis, Y., Pelekis, N., Theodoridis, Y. A transformer-based method for vessel traffic flow forecasting. Geoinformatica (2024). DOI  

  • Graser, A., Jalali, A., Lampert, J., Weißenfeld A., Janowicz, K. MobilityDL: a review of deep learning from trajectory data Geoinformatica (2024). DOI  

  • Duarte M.M.G., Sakr, M. An experimental study of existing tools for outlier detection and cleaning in trajectories Geoinformatica (2024). DOI  

  • Abboud, M., Taher, Y., Zeitouni, K., Olteanu-Raimond A.M. How opportunistic mobile monitoring can enhance air quality assessment? Geoinformatica (2024). DOI  

  • Oakley, J., Conlan, C., Demirci, G.V., Sfyridis, A., Ferhatosmanoglu, H. Foresight plus: serverless spatio-temporal traffic forecasting Geoinformatica (2024). DOI  

    • BMDA'2021 SI (link):

    • Brandoli, B., Raffaetà, A., Simeoni, M. et al. From multiple aspect trajectories to predictive analysis: a case study on fishing vessels in the Northern Adriatic sea. Geoinformatica 26, 551–579 (2022). DOI  

    • Nanni, M., Guidotti, R., Bonavita, A. et al. City indicators for geographical transfer learning: an application to crash prediction. Geoinformatica 26, 581–612 (2022). DOI  

    • El Hafyani, H., Abboud, M., Zuo, J. et al. Learning the micro-environment from rich trajectories in the context of mobile crowd sensing. Geoinformatica 28, 177–220 (2024). DOI  

    • Tritsarolis, A., Chondrodima, E., Tampakis, P. et al. Predicting Co-movement patterns in mobility data. Geoinformatica 28, 221–243 (2024). DOI  

    • Ntoulias, E., Alevizos, E., Artikis, A. et al. Online fleet monitoring with scalable event recognition and forecasting. Geoinformatica 26, 613–644 (2022). DOI  

    BMDA'2020 SI (link):

    • Tritsarolis, A., Chondrodima, E., Tampakis, P., Pikrakis, A., Theodoridis, Y. Predicting Co-movement patterns in mobility data. Geoinformatica (2022). DOI  

    • de Oliveira e Silva, R.A., Cui, G., Rahimi, S.M. et al. Personalized route recommendation through historical travel behavior analysis. Geoinformatica (2021). DOI  

    • Carlini, E., de Lira, V., Soares, A. et al. Understanding evolution of maritime networks from automatic identification system data. Geoinformatica (2021). DOI  

    • Bonavita, A., Guidotti, R. & Nanni, M. Individual and collective stop-based adaptive trajectory segmentation. Geoinformatica (2021). DOI  

    BMDA'2019 SI:

    • Etemad, M., Soares, A., Etemad, E. et al. SWS: an unsupervised trajectory segmentation algorithm based on change detection with interpolation kernels. Geoinformatica (2020). DOI  

    • Kontarinis, A., Zeitouni, K., Marinica, C. et al. Towards a semantic indoor trajectory model: application to museum visits. Geoinformatica (2021). DOI  

    • Koutroumanis, N., Santipantakis, G.M., Glenis, A. et al. Scalable enrichment of mobility data with weather information. Geoinformatica (2020). DOI  

    • Makris, A., Tserpes, K., Spiliopoulos, G. et al. MongoDB Vs PostgreSQL: A comparative study on performance aspects. Geoinformatica (2020). DOI  

    • Varlamis, I., Kontopoulos, I., Tserpes, K. et al. Building navigation networks from multi-vessel trajectory data. Geoinformatica 25, 69–97 (2021). DOI  

    BMDA'2018 SI:

    • Nikitopoulos, P., Vlachou, A., Doulkeridis, C. et al. Parallel and scalable processing of spatio-temporal RDF queries using Spark. Geoinformatica (2019). DOI  

    • Chatzikokolakis, K., Zissis, D., Spiliopoulos, G. et al. A comparison of supervised learning schemes for the detection of search and rescue (SAR) vessel patterns. Geoinformatica(2019). DOI  


    Steering Committee

    * Nikos Pelekis, Data Science Lab., University of Piraeus, Greece (email) 

    * Chiara Renso, ISTI-CNR, Pisa, Italy (email) 

    * Yannis Theodoridis, Data Science Lab., University of Piraeus, Greece (email) 


    Acknowledgments

    The BMDA workshop series has been partially supported by a number EU Horizon 2020 projects and grants, including: 



















































    Past Events

    BMDA 2024


    BMDA 2020 remote


    BMDA 2019


    BMDA 2018