Scientific publications

Journal publications

Mobility-based Big Data: Integration and Processing

  • Tampakis, P., Doulkeridis, C., Pelekis, N., Theodoridis, Y. 2020. “Distributed Subtrajectory Join on Massive Datasets.” ACM Digital Library. Published version: https://dl.acm.org/doi/10.1145/3373642 
  • Fu, C., Huang, H., & Weibel, R. 2020. Adaptive simplification of GPS trajectories with geographic context–a quadtree-based approach. International Journal of Geographical Information Science, 1-28. Click here for the published version.
  • Doulkeridis, C. 2020. Scalable Enrichment of Mobility Data with Weather Information, GeoInformatica. (currently under revision)

Mobility-based Big Data: Analytics

Mobility-based Big Data: Visual Analytics

  • Markovic, N., Sekula, P., Vander Laan, Z., Andrienko, G., Andrienko, N. (2018). Applications of Trajectory Data From the Perspective of a Road Transportation Agency: Literature Review and Maryland Case Study. IEEE Transactions on Intelligent Transportation Systems (Early Access), 1-12. doi.org/10.1109/TITS.2018.2843298.
    Pre-print in open access: http://openaccess.city.ac.uk/19987/7/main.pdf
  • Christopher Collins, Natalia Andrienko, Tobias Schreck, Jing Yang, Jaegul Choo, Ulrich Engelke, Amit Jena, Tim Dwyer, “Guidance in the human-machine analytics process”, Visual Informatics, 2018, vol. 2(3), pp.166-180.
    Published version: https://doi.org/10.1016/j.visinf.2018.09.003 (open access)
  • Jie Li, Siming Chen, Kang Zhang, Gennady Andrienko, and Natalia Andrienko, “COPE: Interactive Exploration of Co-occurrence Patterns in Spatial Time Series”, IEEE Transactions on Visualization and Computer Graphics, 2018.
    Pre-print: http://geoanalytics.net/and/papers/tvcg18.pdf
    Published version: https://doi.org/10.1109/TVCG.2018.2851227
  • Shixia Liua, Gennady Andrienko, Yingcai Wu, Nan Cao, Liu Jiang, Conglei Shi, Yu-Shuen Wang, Seokhee Hong, “Steering Data Quality with Visual Analytics: the Complexity Challenge”, Visual Informatics, 2019
    Published version: https://doi.org/10.1016/j.visinf.2018.12.001 (open access)

Conference publications

Mobility-based Big Data: Integration and Processing

  • Koutroumanis N., Santipantakis G., Glenis A., Doulkeridis C.,Vouros G. , “Integration of Mobility Data with Weather Information”EDBT/ICDT workshops 2019, Lisbon, Portugal, 2019.
    Download here: http://ceur-ws.org/Vol-2322/BMDA_1.pdf
  • P. Nikitopoulos, G.A. Sfyris, A. Vlachou, C. Doulkeridis, O. Telelis: “Parallel and Distributed Processing of Reverse Top-k Queries”, In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019).
  • Koutroumanis N., Nikitopoulos P., Vlachou A., Doulkeridis C. (2019): NoDA: Unified NoSQL Data Access Operators for Mobility Data. In: Proceedings of the 16th International Symposium on Spatial and Temporal Databases (SSTD’19). (to appear)
  • Nikitopoulos, P., Sfyris, G.A., Vlachou, A., Doulkeridis, C., Telelis, O. “Pruning Techniques for Parallel Processing of Reverse Top-k Queries.” Distributed and Parallel Databases (Springer), 2020. Published version: DOI 10.1007/s10619-020-07297-9
  • Fu, C., & Weibel, R. (2019, November). “Cross-scale Spatial Enrichment of Trajectories for Speeding Up Similarity Computing.” In 15th International Conference on Location-Based Services (p. 135). Published version: http://repositum.tuwien.ac.at/obvutwoa/content/titleinfo/4526195

Mobility-based Big Data: Analytics

  • P. Nikitopoulos, A.-I. Paraskevopoulos, C. Doulkeridis, N. Pelekis, Y. Theodoridis, “Hot Spot Analysis over Big Trajectory Data”, In Proceedings of the 2018 IEEE International Conference on Big Data (IEEE BigData 2018).
    Download here: https://www.ds.unipi.gr/prof/cdoulk/papers/bigdata18.pdf
  • Katzouris, N., Michelioudakis, E., Artikis, A., & Paliouras, G. (2018, September). Online learning of weighted relational rules for complex event recognition. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 396-413). Springer, Cham. Download here: http://www.ecmlpkdd2018.org/wp-content/uploads/2018/09/154.pdf
  • Guidotti, R, Monreale A, Cariaggi L (2019). Investigating Neighborhood Generation Methods for Explanations of Obscure Image-Classifiers, In: Yang Q., Zhou ZH., Gong Z., Zhang ML., Huang SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science, vol 11439. Springer, Cham.
    Published version::  https://doi.org/10.1007/978-3-030-16148-4_5
  • Tsilionis E., Koutroumanis N., Nikitopoulos P., Doulkeridis C., Artikis A. (2019): Online Event Recognition from Moving Vehicle. In: Proceedings of the 35th International Conference on Logic Programming (ICLP’19). (conditionally accepted).
  • Nanni, M., Longhi, L. (2019): Vehicle mobility data analysis and Individual Mobility Networks for crash prediction. 
    Published version: “Vehicle mobility data analysis and Individual Mobility Networks for crash prediction.”
  • Theodoridis Y. (2020): Learning from Our Movements – The Mobility Data Analytics Era. In: Tserpes K., Renso C., Matwin S. (eds) Multiple-Aspect Analysis of Semantic Trajectories. MASTER 2019. Lecture Notes in Computer Science, vol 11889. Springer, Cham. Published version: https://doi.org/10.1007/978-3-030-38081-6_1
  • Guidotti, R., Nanni, M. (2020). Crash Prediction and Risk
    Assessment with Individual Mobility Networks. The 21st IEEE International Conference on Mobile Data Management  (MDM 2020). To appear soon. Pre-print version: https://trackandknowproject.eu/wp-content/uploads/2020/05/MDM_2020___Crash_Prediction.copyright.pdf
  • Yeghikyan, G., Opolka, F., Lepri, B., Nanni, M., Lio, P. Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks. 2020 IEEE International Conference on Smart Computing (SMARTCOMP). To appear soon. Pre-print version: https://trackandknowproject.eu/wp-content/uploads/2020/05/SMARTCOMP_2020_Learning_Mobility_Flows_from_Gnns.copyright.pdf
  • Agnese Bonavita, Riccardo Guidotti, Mirco Nanni. “Self-Adapting Trajectory Segmentation.” In EDBT/ICDT Workshop on Big Mobility Data Analytics (BMDA 2020), CEUR, vol 2578, 2020. Published version: http://ceur-ws.org/Vol-2578/BMDA3.pdf
  • Riccardo Guidotti, Mirco Nanni, Francesca Sbolgi. “Data-Driven Location Annotation for Fleet Mobility Modeling.” In EDBT/ICDT Workshop on Big Mobility Data Analytics (BMDA 2020), CEUR, vol 2578, 2020. Published version: http://ceur-ws.org/Vol-2578/BMDA2.pdf
  • Omid Isfahani Alamdari, Mirco Nanni, Roberto Trasarti, Dino Pedreschi. “Towards In-Memory Sub-Trajectory Similarity Search.” In EDBT/ICDT Workshop on Big Mobility Data Analytics (BMDA 2020), CEUR, vol 2578, 2020. Published version: http://ceur-ws.org/Vol-2578/BMDA9.pdf
  • Petros Petrou, Panagiotis Nikitopoulos, Panagiotis Tampakis, Apostolos Glenis, Nikolaos Koutroumanis, Georgios M. Santipantakis, Kostas Patroumpas, Akrivi Vlachou, Harris V. Georgiou, Eva Chondrodima, Christos Doulkeridis, Nikos Pelekis, Gennady L. Andrienko, Fabian Patterson, Georg Fuchs, Yannis Theodoridis, George A. Vouros: ARGO: A Big Data Framework for Online Trajectory Prediction. SSTD 2019: 194-197. Published version: https://www.ds.unipi.gr/prof/cdoulk/papers/sstd19b.pdf
  • Katzouris N. and Artikis A., WOLED: A Tool for Online Learning Weighted Answer Set Rules for Temporal Reasoning Under Uncertainty. In Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2020. To appear soon.

Mobility-based Big Data: Visual Analytics

Books

  • Andrienko, G., Andrienko, N., Patterson, F., Chen, S., Weibel, R., Huang, H., Doulkeridis, C., Georgiou, H., Pelekis, N., Theodoridis, Y., Nanni, M., Longhi, L., Koumparos, A., Yasar, A. and Kureshi, I.
    “Visual Analytics for Characterizing Mobility Aspects of Urban Context.” Wenzhong Shi, Michael Goodchild, Michael Batty, Mei-Po Kwan, Anshu Zhang (Eds.) Urban Informatics. Springer, 2020.
    Pre-print: http://geoanalytics.net/and/papers/VA-urban20.pdf
  • Andrienko, N., Andrienko, G. “Visual Analytics of Vessel Movement.”
    Alexander Artikis and Dimitris Zissis (Eds.) Maritime Informatics. Springer, 2020.
    Pre-print: http://geoanalytics.net/and/papers/VA-vessels20.pdf
  • Andrienko, N., Andrienko, G., Fuchs, G., Slingsby, A., Turkay, C., Wrobel, S. Visual Analytics for Data Scientists. Springer, 2020.
    Published version: https://www.springer.com/gp/book/9783030561451