CALL FOR PAPERS
An ever-increasing number of diverse, real-life applications, ranging from mobile to social media apps and surveillance systems, produce massive amounts of spatio-temporal data representing trajectories of moving objects. The fusion of those trajectories, commonly represented by timestamped location sequence data (e.g. check-ins and GPS traces), with generally available and semantic-rich data resources can result in an enriched set of more comprehensive and semantically significant objects. The analysis of these sets, referred to as “semantic trajectories”, can unveil solutions to traditional problems and unlock the challenges for the advent of novel applications and application domains, such as transportation, security, health, environment and even policy modeling.
Despite the fact that the semantic trajectories concept is not new, we are now witnessing an increasing complexity in the forms and heterogeneity of the enrichment process producing new kinds of trajectory objects. These new objects call for novel methods that can properly take into account the multiple semantic aspects defining this new form of movement data. It is the very nature of the semantic trajectories that makes this analysis challenging. For instance, the data sources and formats are largely heterogeneous, placing hurdles in the fusion process; or their volumes are too large to process them in conventional ways. In the other cases the state of the semantic trajectories is updated at such a rapid pace, that it is very hard to explore them so as to get an indication of their latent semantics, or even process them in a consistent way since they cannot be stored. Another typical problem is with their unreliable and erroneous nature, where signals are arriving in a mixed order, with gaps and even errors. Similarly, the multiple aspects nature of semantic trajectories increases the difficulty of trajectory pattern mining. With the maturing and scaling of Machine Learning and Data Mining technologies as potential tools to address the above-mentioned challenges, and the ubiquity of the trajectory data, the topic of the proposed Workshop fits very well the overall research theme of ECML PKDD.
This workshop seeks to solicit original contributions that address a wide range of theoretical and practical issues related to trajectories and especially to semantic trajectories, including, but not limited to:
- Machine learning and Deep Learning with [semantic] trajectory data
- Time series analysis for [semantic] trajectories
- Graph-based methods for [semantic] trajectory analysis
- Visual methods for [semantic] trajectories analysis
- Indexing methods for [semantic] trajectory analysis
- Methods for evaluation of [semantic] trajectory analysis, modeling and prediction
- Data models for [semantic] trajectories
- High-impact results from the fusion of trajectories with semantically-rich datasets
- Anomaly Detection, long-term [semantic] trajectory prediction techniques
- Infrastructures and systems to support the [semantic] trajectory analysis
- Big data issues in [semantic] trajectory analysis
- [Semantic] trajectories stream processing
- Applications of the analysis of [semantic] trajectories, e.g. in the area of smart cities
- Ethical, societal and privacy issues in the analysis of semantically rich trajectories
Prof. Yannis Theodoridis, University of Piraeus, Greece
Title: Learning from our movements – The mobility data analytics pipeline
Papers submitted to MASTER2019 should be written in English conforming to the Springer LNCS guidelines. Author instructions, style files and the copyright form can be downloaded here (http://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines).
The maximum length of papers is 16 pages in this format. Over-length papers will be rejected without review (papers with smaller than specified page margins and font sizes will be treated as over-length).
Up to 10 MB of additional materials (e.g. proofs, audio, images, video, data, or source code) can be uploaded with your submission. The reviewers and the program committee reserve the right to judge the paper solely on the basis of the 16 pages of the paper; looking at any additional material is at the discretion of the reviewers and is not required.
All accepted papers will be published in a special section of the LNCS conference proceedings and become openly accessible. The costs for gold open access publishing will be covered by the project “MASTER”.
Selected papers will be invited in a special issue in a journal such as International Journal of Geographical Information Science (IJGIS). The exact journal will be announced soon.
- Chiara Renso, CNR, Pisa, Italy (http://hpc.isti.cnr.it/~renso/)
- Stan Matwin, Dalhousie University, Halifax, Canada (https://web.cs.dal.ca/~stan/)
- Konstantinos Tserpes, HUA, Athens, Greece (http://www.dit.hua.gr/~tserpes)
- Prof. Demetrios Zeinalipour-Yazti, University of Cyprus
- Dr. Gennady Andrienko, Fraunhofer Institute IAIS
- Prof. Goce Trajcevski, Iowa University
- Prof. Maria Luisa Damiani, University of Milan
- Prof. Matthias Renz, University of Kiel
- Prof. Ralf Hartmut Guting, Fernuniversitat Hagen
- Prof. Cyrus Shahabi, University of Southern California
- Prof. Cyril Ray, Naval Academy Research Institute
- Prof. Dimitris Kotzinos, University of Cergy-Pontoise
- Prof. Sergio Illari, University of Zaragoza
- Dr. Mirco Nanni, CNR – Nationale Research Council of Italy
- Dr. Anna Monreale, University of Pisa
- Prof. Dimitrios Gunopulos, University of Athens
- Prof. Luis Torgo, Dalhousie U.
- Dr. Sebastien Gambs, University du Quebec Montreal
- Prof. Latifa Oukhellou, IFSTTAR
- Dr. Angelo Furno, IFSTTAR
- Dr. Fabio Valdés, Fernuniversität Hagen
- Prof. Magdalini Eirinaki, SJSU
- Dr. Amilcar Soares, Dalhousie University
- Prof. Vania Bogorny Federal University of Santa Catarina
- Prof. Jose Fernandes de Macedo Federal University of Ceara’
Sponsorship & acknowledgment
The workshop is organized and sponsored by the project MASTER. MASTER (http://www.master-project-h2020.eu) project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 777695.
For further inquiries please contact: tserpes-at-hua-dot-gr