BDDMC ’19 – In conjunction with the 33rd IEEE IPDPS ’19

The technological advancements and the ever-growing number of smart devices have increased the amount of mobile-generated data exponentially in the last years. This Big Data comprises digital content like texts, photos, videos, social connections and reactions, as well as sensor measurements, and can be turned into intelligence for solving real-life problems, such as traffic congestion, fraud detection, weather monitoring, and so forth.
New hardware and software tools have been proposed to advance the traditional data processing solutions in order to keep the pace with the Big Data generation. However, these solutions are usually tailored to the conventional commodity hardware. Big Data processing architectures for mobile de- vices have started to become a reality only recently, due to the widespread adoption of mobile devices and cloud computing in combination with the increasing storage and processing capabilities in both the edge and the cloud.
Moreover, thanks to the multitude of wireless interfaces, mobile devices can collaborate with each other to solve different problems. This recent topic, known as Device-to-Device (D2D) computing and edge/fog computing, has paved the way for innovative distributed applications, and contributions on making them faster, more energy efficient, as well as robust to challenges in connectivity are needed. Another open research problem is related to development of mobile computing frameworks that reward users contributing with their devices’ resources and sidelines the selfish ones. Finally, since much of the mobile-generated data is of sensitive nature, anonymizing and protecting such data should be of crucial concern.

Theme of the Workshop

The goal of this workshop is to solicit high-quality research articles on new solutions on advancing the processing of Big Data via mobile computing as well as on new mobile applications, systems, and services that utilize the intelligence generated by Big Data. Potential topics include, but are not limited to, the following:

  • Big Data driven mobile applications, systems, and services.
  • System architecture and theory for Big Data driven mobile computing.
  • Architectures for Big Data processing in mobile computing.
Privacy and security of Big Data in mobile computing.
  • Grid/Cloud/HPC/GPU techniques for Big Data processing in mobile computing.
  • Geographically referenced Big Data in mobile computing.
  • Data driven offloading for mobile traffic and computation.
  • Distributed computation on mobile devices.
  • Machine learning on mobile devices.
  • Collection, processing, and visualization of mobile-generated data.
  • Real-time and online mobile-data processing.
  • Distributed information spreading on mobile devices.
  • Incentive schemes for D2D computation.

Workshop Committee

  • Steering Committee
    • Ian Foster (Director of Argonne’s Data Science and Learning Division, University of Chicago, USA).
    • Pan Hui (Nokia Chair Professor in Data Science Department of Computer Science University of Helsinki Helsinki, Finland –
  • TPC co-chairs
    • Sokol Kosta (Aalborg University Copenhagen, Denmark –
    • Cedomir Stefanovic (Aalborg University Copenhagen, Denmark –
  • Technical Program Committee
    • Paulo Romero Martins Maciel (Federal University of Pernambuco, Brasil).
    • Yu Xiao (Aalto University, Finland).
    • Francisco Airton Pereira Da Silva (Federal University of Piaui, Brasil).
    • Reza Tadayoni (Aalborg University Copenhagen, Denmark).
    • Jesus Carretero (University of Madrid Carlos III, Spain).
    • Jose Luis Gonzalez Compean (Cinvestav Unidad Tamaulipas, Mexico).
    • Dimitris Chatzopoulos (Hong Kong University of Science and Technology, Hong Kong).
    • Leonardo Tonetto (Technical University of Munich, Germany).
    • Vittorio Cozzolino (Technical University of Munich, Germany).
    • Huber Flores (University of Helsinki, Finland).
    • Giancarlo Fortino (University of Calabria, Italy).
    • Aaron Yi Ding (Delft University of Technology, Netherlands).
    • Jin Wei (University of Akron, US).
    • Pirathayini Srikantha (Western University, CA).
    • Dubravko Culibrk (University of Novi Sad, Serbia).
    • Ivor Spence (Queen’s University of Belfast, United Kingdom).
    • Francisco Javier Garcia Blas (University of Madrid Carlos III, Spain).
    • Iakovos Mavroidis (Foundation for Research and Technology Hellas (FORTH), Greece).
    • Antonis Argyros (Foundation for Reaserch and Technology Hellas (FORTH), Greece).
  • Publicity chairs
    • Jiayu Shu (Hong Kong University of Science and Technology, Hong Kong –
    • Dimitris Deyannis (Foundation for Research and Technology Hellas (FORTH), Greece –


Authors are encouraged to submit papers describing original and unpublished research, not currently under review in other venues. Submitted manuscripts should be PDF files up to 10 pages for full papers and 8 pages for short papers, formatted using single-spaced double-column pages, 10-point size font on 8.5×11 inch pages (IEEE conference style), including figures, tables, and references.

The IEEE conference style templates for MS Word and LaTeX provided by IEEE eXpress Conference Publishing are available for download. See the latest versions here.

The reviews will be single blind. The first page must contain an abstract, the name(s) and affiliation(s) of the author(s), as well as the corresponding contact information. At least one of the authors of every accepted paper must register and present the paper at the workshop.

Keynote Speakers

Dr. Ilkay Altintas, Director for the Center of Excellence in Workflows for Data Science at the San Diego Supercomputer Center (SDSC), UCSD, USA. Since joining SDSC in 2001, she has worked on different aspects of scientific workflows as a principal investigator and in other leadership roles across a wide range of cross-disciplinary NSF, DOE, NIH and Moore Foundation projects. She is a co-initiator of and an active contributor to the open-source Kepler Scientific Workflow System, and the co-author of publications related to eScience at the intersection of scientific workflows, provenance, distributed computing, bioinformatics, observatory systems, conceptual data querying, and software modeling.