MegaData Overview

Under the exponential growth of Big Data (BD) from different sources, managing, engineering, and designing Data systems, gaining meaningful insights is a significant challenge. The current generation of data engineers and architects works tirelessly to satisfy the accelerating demand for data-driven innovations. Questions like: How to thrive data as the foundation for advanced Databases and Information Systems? And what will the next generation of Data systems look like? Will lead the discussion on the latest trends in modern data systems. MegaData workshop aims to report on the advances and trends in BD deployment models and environments from both the infrastructure and application levels. Papers presenting recent results, research issues, practical applications, case studies, and industrial implementations are welcome. Moreover, the submission of ongoing research, position, visionary, and student papers are encouraged to fuel up the discussion.

MegaData Aims

Data is growing explosively, and several systems have emerged to store, process, and analyze such large-scale amounts of data. These “Big data systems” are fast evolving to meet the practitioners’ demand from both industry and academia alike. Examples include the NoSQL systems, Hadoop stack, Apache Spark, data analytics platforms, search and indexing platforms, and deployment infrastructures. These systems address needs for structured and unstructured data across a wide spectrum of domains and applications ranging from NoSQL and batch processing to micro-batch processing and stream data processing frameworks.

The MegaData workshop’s objective is to bring together researchers, practitioners, system administrators, system programmers, and others interested in sharing and presenting their perspectives on the effective management of big data systems. The focus of the workshop is on a novel and practical, systems-oriented work. MegaData offers an opportunity to showcase the latest advances in this area and discuss and identify future directions and challenges in management and engineering of big data systems.

Topics

Papers are solicited on all aspects of big data management. Specific topics of interest include, but are not limited, to the following:

• Resource management and scheduling mechanisms
• System tuning/auto-tuning and configuration management
• Auto Scaling and elastic scaling challenges and opportunities
• Unified management of ‘data in motion’ and ‘data at rest.’
• Dealing with both structured and unstructured data
• Holistic management across hardware and software
• Emerging hardware/software technologies such as shared memory, hyperthreading, and
• Domain-specific challenges in the cloud, sensor networks, streaming analytics, cyber-physical systems
• Emerging deployment models in IoT, IoT-to-Cloud, Edge/fog deployment, HPC
• Scalable architectures for data storage, archival, and virtualization
• Performance benchmarking and workload studies
• Advances in data storage models, including object stores and key-value stores
• Techniques for data integrity, availability, reliability, and fault tolerance
• Productivity tools for data-intensive computing, data mining, and knowledge discovery
• Application of emerging big data frameworks towards scientific computing and analysis
• Enabling cloud and container-based models for scientific data analysis
• Tools and techniques for managing data movement among computing and data-intensive components

Program Committee Members (to be completed)

• Pablo Rodríguez-Mier, INRAE, France
• Victor M. Muñoz , Universitat Oberta de Catalunya, Spain
• Manisha Sirsat, INESC, Portugal
• Arturo Gonzalez-Escribano, Universiadad de Valladolid, Spain
• James Benson University of Texas at San Antonio, USA
• Rosa Filgueira, EPCC, The University of Edinburgh, UK
• Imed Romdhani, Edinburgh Napier University, UK
• Sattam Almatarneh, Middle East University, Jordan.
• Said Alawadi, Uppsala University, Sweden.
• Syed Attique Shah, University of Tartu, Estonia
• Pablo Caderno, University of Santiago de Compostela, Spain
• Ahmad Aburomman, University de A Crouna, Spain
• Maanak Gupta Assistant Professor, Tennessee Technological University, USA
• Mehdi Gheisari Guangzhou University, China
• Mohamed Ragab, Tartu University, Estonia
• Houshyar Honar Pajooh, Masey University, New Zealand
• Xoan C. Pardo, Universidade da Coruña, Spain
• Jose R.R. Viqueira, Universidade de Santiago de Compostela, Spain
• Fahed Alkhabbas, Malmö University, Sweden

Publication

Workshop papers will be published in the Springer Communications in Computer and Information Science (CCIS) series. The best workshop papers will be invited to a special issue of the journal: Computer Science and Information Systems (ComSIS)

Important dates

• Paper submission deadline: May 16, 2022
• Notification of acceptance: June 27, 2022
• Camera-ready due: July 15, 2022
• Workshop day: September 05, 2022

Paper Submission

Authors are invited to submit Short papers (up to 10 pages) and regular papers (up to 14 pages). Papers must be submitted via Easy Chair.

Select make a new submission then select “Advances in Data Systems Management, Engineering, and Analytics” from the tracks list.

Templates, sample files, and useful links can be found in the LaTeX and Word files for frontmatter (zip) section. 

Authors should consult Springer’s authors’ guidelines and use their proceedings templates, either for LaTeX or for Word, to prepare their papers. Springer encourages authors to include their ORCIDs in their papers. The corresponding author of each paper, acting on behalf of all of the authors of that paper, must complete and sign a Consent-to-Publish form.

The corresponding author signing the copyright form should match the corresponding author marked on the paper. Once the files have been sent to Springer, changes relating to the papers’ authorship cannot be made.