DBDM 2024: The 9th IEEE International Workshop on Distributed Big Data Management

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 2024 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 2024 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. 

 Big Data Management is of relevant interest at now, and it can be considered as one of the most emerging research topics we deal with. In particular, the management of Big Data in distributed settings (like Clouds) is demanding for innovative models, techniques and algorithms capable of dealing with the well-known Vs of such kind of data. 

Indeed, traditional approaches are not suitable to manage Big Data in distributed environments, due to the Volume, Velocity and Variety of Big Data. Starting from this evidence, recently we experienced novel proposals that are trying to creating applications and systems that, running on top of distributed settings like Clouds, effectively and efficiently manage Big Data as to support a wide range of contexts, among which analytics, knowledge discovery and cybersecurity methods are just some relevant examples. 

Despite these initiatives, lot of work still needs to be done in such research area, as it encompasses a large collection of topics ranging from data management algorithms to high-performance techniques. Topics are both of theoretical nature (e.g., managing uncertain and imprecise distributed Big Data) and practical nature (e.g., Big Data dissemination in distributed environments). 

Following the great success of the previous six editions, four co-located with IEEE/ACM CCGRID conference series and two co-located with IEEE COMPSAC, the aim of the 9th International Workshop on Distributed Big Data Management (DBDM 2024) is to capture the new research trends and results in terms of models, techniques, algorithms, architecture and applications for the management of Big Data in distributed environments. This workshop will also identify potential research directions and technologies that will drive innovations within this domain. We anticipate this workshop to establish a pathway for the development of future-generation large-scale Big Data management systems. 

Workshop Theme and Topics

The DBDM 2024 workshop focuses on all the research aspects of distributed management of Big Data. Including but not limited to:

  • 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 

Paper Templates

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

IEEE Conference Publishing Policies

All submissions must adhere to IEEE Conference Publishing Policies.

IEEE Cross Check

All submission will be screened for plagiarized material through the IEEE Cross Check portal.

Important Dates – Workshops

Workshop, Fast Abstract, SRS, JC  papers due
April 15, 2024  Extended to April 21

Notification
May 1, 2024

Camera Ready & Registration due
May 25, 2024

Workshop Chairs

Alfredo Cuzzocrea, University of Calabria, Italy
Email: alfredo.cuzzocrea[at]unical.it 

Program Committee

TBA