Learning from our movements – Big Mobility Data Analytics
From raw location recordings to mobility patterns – how can we exploit on the ubiquitous GPS technology in order to get knowledge about our movement behavior? Which are the most representative examples of patterns that can be mined from humans’ mobility datasets? In this course, issues and solutions on Big Mobility Data Analytics (BMDA) are overviewed, including acquisition, storage, processing, and mining aspects. Use cases from urban, maritime, and/or aviation domains will be presented.
Course background knowledge:
The course is about mobility data management and analytics. Necessary background includes undergraduate level knowledge on programming (Python, R, Java, etc.), algorithms (searching and sorting, complexity), database systems (relational design, SQL, indexing, a kind of experience with a real DBMS like Postgres), and GIS (geographical coordinates, map visualization, a kind of experience with a real GIS like QGIS).
Suggested reading list:
– Trajectory Data Mining: An Overview
– Semantic trajectories modeling and analysis
– Mobility Data Management and Exploration
– Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods
– Simulating Our LifeSteps by Example
Tutors:
Yannis Theodoridis
Data Science Lab, University of Piraeus
Yannis Theodoridis is Professor of Data Science and head of the Data Science Lab at the University of Piraeus, Greece. He participates or has participated in several boards, including the sectorial scientific council on Computer Science of the National Council for Research, Technology and Innovation (vice-chair, 2020-), the general assembly of the Hellenic Foundation for Research and Innovation (2017-18), the editorial board of ACM Computing Surveys (2016-19), and the Endowment of the Symposium on Spatial and Temporal Databases – SSTD (2010-). He has also served as general co-chair for SSTD’03 and ECML/PKDD’11, PC vice-chair for IEEE ICDM’08, and PC member for several conferences, including ACM SIGMOD/PODS, IEEE ICDE, ACM SIGKDD, IEEE ICDM, etc. Since 2001, he has been a (co-) principal investigator in several research projects funded through open calls, with MobiSpaces (EU H2020, 2022-25), VesselAI (EU H2020, 2021-23), and Track-and-Know (EU H2020, 2018-20) being the most recent ones. His research interests include Data Science for mobility-related information. He has co-authored three monographs and over 100 refereed articles in scientific journals and conferences, with over 13,000 citations so far, according to Google Scholar. He is ACM Senior member. He holds a Dipl. Eng. (1990) and Ph.D. (1996) in Computer Engineering, both from the National Technical University of Athens (NTUA).
Panagiotis Tampakis
Department of Mathematics and Computer Science, University of Southern Denmark
Panagiotis Tampakis is currently an Assistant Professor at the Department of Mathematics and Computer Science of the University of Southern Denmark.
He has participated in a number of research projects related to Data Science and Big Data, with EU Horizon 2020 funded MASTER (2018-21) and Track-and-Know (2018-20) being the ongoing ones. His publication record includes publications in top venues and journals, such as ICDE, EDBT, PKDD and DMKD. His research interests include mobility data management and mining, big data, and distributed data management.