Welcome to CDF4MD at MDM 2024

Mobile data management deals with diverse data coming from dynamic and potentially heterogeneous data sources. Mobile systems produce myriads of data, including such of different modalities, types, or at different processing stages, summarised by the term domain. With emerging modern technologies, such as Internet-of-Things, these systems and their data get more complex with each day to come. State-of-the-Art (SOTA) methods typically only allow to analyze data sets within a single domain. We are missing data analysis techniques that are designed for fusing multiple pieces from different (scientific) domains in order to gain a more holistic knowledge of complex systems. It is imperative that such explorative data analysis methods are developed to enable domain experts to effectively leverage this holistic knowledge.

This workshop introduces cross-domain fusion (CDF) as an emerging topic to mobile data management. CDF involves identifying patterns, trends, and anomalies in spatio-temporal data, including sensor measurements, satellite imagery, or data generated by scientific models with spatio-temporal context. This offers insights into the underlying processes and mechanisms governing the system under investigation. CDF addresses the key aspects of fusing multiple views from different domains using exploratory data analysis to identify patterns, trends, and anomalies among arbitrary views enabling, for instance, the construction of spatio-temporal digital twin worlds.


9:00 - Welcome
  • Introduction to CDF4MD
9:10 - Session 1
  • Fusing Image Data and Ontologies for Situation Representation in Knowledge Graphs
    Ravindi Iroshinee De Silva (Deakin University)*, Arkady Zaslavsky (Deakin University), Seng Loke (Deakin University), Guang-Li Huang (Deakin University), Prem Jayaraman (Swinburne University of Technology), Ashim Debnath (Deakin University)
  • Proactive Context Caching Based on Situation Prediction for Real-Time Mobile IoT Applications
    Shakthi Y Weerasinghe (Deakin University)*, Arkady Zaslavsky (Deakin University), Seng Loke (Deakin University), Guang-Li Huang (Deakin University)
10:00 - Plenary Session
  • GenAI4MoDA invited speaker
  • Coffee break
11:00 - Session 2
  • Data Fusion Between Land and Sea: Multi-Isotope Fingerprints of Viking Animals and Modern Plants
    Andrea Göhring (Kiel University)*, Mirjam R Bayer (Kiel University), Daniyal Kazempour (Kiel University), Sweety Mohanty (Kiel University), Claudius Zelenka (Kiel University)
  • Final discussion
11:50 - Plenary Session
  • CDF4MD invited speaker
  • Conclusion and remarks

Important Dates (23:59 AoE)

  • Paper Submission

    Authors submit papers until
    April 12, 2024
    April 19, 2024

  • Acceptance Notice

    Authors will be notified until
    May 3, 2024
    May 7, 2024

  • Camera-Ready

    Deadline for camera-ready submissions
    May 15, 2024


  • Matthias Renz

    Dept. of Computer Science
    Kiel University

  • Peer Kröger

    Dept. of Computer Science
    Kiel University

  • Nikos Mamoulis

    Dept. of Computer Science & Engineering
    University of Ioannina

  • Nelson Tavares de Sousa

    Dept. of Computer Science
    Kiel University

  • Yannick Wölker

    Dept. of Computer Science
    Kiel University

Program Committee

  • Amr Magdy (University of California Riverside)
  • Daniyal Kazempour (CAU Kiel)
  • Demetrios Zeinalipour-Yazti (University of Cyprus)
  • Dimitrios Skoutas (Athena Research Center)
  • Dimitris Papadias (HKUST)
  • Dimitris Sacharidis (ULB)
  • Goce Trajcevski (Iowa State University)
  • Martin Werner (TU München)
  • Matthias Schubert (Ludwig-Maximilians-Universität München)
  • Panagiotis Bouros (Johannes Gutenberg University Mainz)
  • Raymond Chi-Wing Wong (The Hong Kong University of Science and Technology)
  • Reynold Cheng (The University of Hong Kong, China)
  • Sanjay Kumar Madria (Missouri University of Science & Technology, USA)
  • Theodoros Chondrogiannis (University of Konstanz)
  • Yannis Theodoridis (University of Piraeus)
  • Yaron Kanza (AT&T Labs-Research)