The 6th IEEE International Workshop on Big Data Management (DBDM 2021)

Call for Papers


In the last few years there have been an increasing number of initiatives to develop distributed Big Data management platforms for diversified application domains, from e-science to large-scale sensor network monitoring. Many national and international research projects in academia, industry and government focus on the development and deployment of distributed Big Data management systems. Apart from companies working in the Big Data domain, there are also initial important investments in this field from social networks to Web advertisement, from astronomic data management to biological data management, as so forth. There are already several workshops oriented to Big Data management systems, however none of them focus on distributed Big Data management as major topic, where distributed models, techniques, algorithms and architectures play the first-class role. Thus the establishment of a new venue in which the definition of novel models, techniques, algorithms and architectures for supporting the distributed management of Big Data is discussed and disseminated, may enable new research ideas and launch new business pathways.

The DBDM 2021 workshop aims to synergistically connect the research community and industry practitioners. It provides an international forum where scientific domain experts, database researchers, practitioners and developers can share their findings in theoretical foundations, current methodologies, and practical experiences on distributed management of Big Data. DBDM 2021 provides a stimulating environment to encourage discussion, fellowship, and exchange ideas in all aspects of research related to distributed management of Big Data, including both original research contributions and insights from practical system design, implementation and evaluation, along with new research directions and emerging application domains in the area of distributed management of Big Data. The realistic outcome expected by DBDM 2021 is to define new problems in the context of distributed management of Big Data and achieving consolidated solutions to already-known problems. Also, another relevant goal it to create a focused community of scientists who create and drive interest in the area of distributed management of Big Data, and possibly to continue to contribute to the success of the event across years.

The DBDM 2021 workshop focuses on all the research aspects of distributed management of Big Data, including:

  • Distributed Big Data: Fundamentals
  • Distributed Big Data: Modelling
  • Distributed Big Data: Statistical Approaches
  • Distributed Big Data: Novel Paradigms
  • Distributed Big Data: Innovative Protocols
  • Distributed Big Data: Algorithms
  • Distributed Big Data: Query Optimization
  • Distributed Big Data: Non-Conventional Environments (e.g., Spatio-Temporal Data, Streaming Data, Cloud Data, Probabilistic Data, Uncertain Data)
  • Distributed Big Data: Systems
  • Distributed Big Data: Architectures
  • Distributed Big Data: Advanced Topics (e.g., NoSQL Databases)
  • Distributed Big Data: Case Studies and Applications
  • Innovative Models for Big Data Management in Distributed Settings
  • Innovative Techniques for Big Data Management in Distributed Settings
  • Innovative Algorithms for Big Data Management in Distributed Settings
  • Innovative Architectures for Big Data Management in Distributed Settings
  • Query Processing Approach for Big Data in Distributed Settings
  • Approximate Query Processing of Big Data in Distributed Settings
  • Uncertain and Imprecise Big Data Management in Distributed Settings
  • Privacy Preserving Big Data Management in Distributed Settings
  • Secure Big Data Management in Distributed Settings
  • Scalable Big Data Analytics in Distributed Settings
  • Data Warehousing over Big Data in Distributed Settings
  • OLAP over Big Data in Distributed Settings
  • Big Graph Data Management in Distributed Settings
  • Big RDF Data Management in Distributed Settings
  • Streaming Big Data Management in Distributed Settings
  • Virtual Big Data Management in Distributed Settings
  • Indexing Approaches for Big Data in Distributed Settings
  • Theoretical Models for Big Data Representation in Distributed Settings
  • Big Data Exchange Models and Algorithms in Distributed Settings
  • Big Data Fusion Models and Algorithms in Distributed Settings
  • Big Data Integration Models and Algorithms in Distributed Settings
  • Big Data Availability Models and Algorithms in Distributed Settings
  • Big Data Reliability Models and Algorithms in Distributed Settings

Important Dates


Workshop papers due: 21 April 2021

Workshop paper notifications: 15 May 2021

Camera-ready and registration due: 31 May 2021


Authors are invited to submit original, unpublished research work, as well as industrial practice reports. Simultaneous submission to other publication venues is not permitted.  In accordance with IEEE policy, submitted manuscripts will be checked for plagiarism. Instances of alleged misconduct will be handled according to the IEEE Publication Services and Product Board Operations Manual.

Please note that in order to ensure the fairness of the review process, COMPSAC follows the double-blind review procedure. Therefore we kindly ask authors to remove their names, affiliations and contacts from the header of their papers in the review version. Please also redact all references to authors’ names, affiliations or prior works from the paper when submitting papers for review. Once accepted, authors can then include their names, affiliations and contacts in the camera-ready revision of the paper, and put the references to their prior works back.

Formatting


Workshop papers are limited to 6 pages. Page limits are inclusive of tables, figures, appendices, and references. Workshop papers can add an additional 2 pages with additional page charges ($250USD/page).

Paper Templates


IEEE Paper templates are available in MS Word 2003 and LaTex. All submissions must use US 8.5×11 letter page format.



Workshop Organizers


Alfredo Cuzzocrea, University of Calabria, Italy
Email: alfredo.cuzzocrea@unical.it

Program Committee


Ashraf Aboulnaga, Qatar Computing Research Institute, Qatar

Divy Agrawal, University of California, Santa Barbara, USA

Gagan Agrawal, Ohio State University, USA

Michael Berthold, Uni Konstanz and KNIME, Zurich, Germany

Albert Bifet, Huawei Noah’s Ark Lab, China

Francesco Bonchi, Yahoo! Research, USA

Carlos Castillo, Qatar Computing Research Institute, Qatar

James Cheng, The Chinese University of Hong Kong, Hong Kong

Geoffrey Fox, Indiana University, United States

Anastasios Gounaris, Aristotle University of Thessaloniki, Greece

Alexandru Iosup, Delft University of Technology, Netherlands

Vana Kalogeraki, Athens University of Economics and Business, Greece

George Karypis, University of Minnesota, USA

Tao Li, Florida International University, USA, USA

Paul Lu, University of Alberta, Canada

Emmanuel Müller, Karlsruhe Institute of Technology, Germany

Dimitrios Nikolopoulos, Queen’s University of Belfast, Ireland

Peter Pietzuch, Imperial College London, UK

Qian Zhu, UMBC, USA

Arno Wacker, University of Kassel, Germany

Xiaoyan Yang, ADSC, Singapore

Hill Zhu, Florida Atlantic University, USA