The Third International Workshop on Advanced Data Systems Management, Engineering, and Analytics (MegaData)

MegaData: where the Edge meets the Cloud

Bangalore, India. May 1, 2023

MegaData Chairs

  • Yaser Jararweh, Computer Science Department, Jordan University of Science and Technology
  • Feras M. Awaysheh, Assistant Professor of Big Data Systems, University of Tartu,
    Estonia https://bigdata.cs.ut.ee/feras-m-awaysheh Contact: feras.awaysheh@ut.ee
  • Moath Jarrah, Assistant Professor of computer science. School of Technology, Eastern
    Illinois University, USA Contact: mhjarrah@eiu.edu

Publicity Chair

  • Anastasija Nikiforova, Assistant Professor of Information system. University of Tartu, Estonia https://anastasijanikiforova.com/ Contact: nikiforova.anastasija@gmail.com

Publication Chair

Web Master

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

Big Data (BD) platforms have a long tradition of leveraging trends and technologies from the broader computing community. For several years, dedicated servers over the cloud have been employed as the dominant paradigm. However, in recent years, that model was shifted towards the network Edge, closer to data sources. MegaData covers the area of BD operations (management, engineering, and analytics) within Cloud and Edge computing models. Also, it aims to report on the advances and trends in BD deployment architectures from both the infrastructure and application levels. Papers presenting recent results, research issues, practical applications, case studies, and industrial implementations are welcome.

Specific topics of interest include, but are not limited, to the following:

  • Resource management and scheduling mechanisms for data systems
  • Auto Scaling and elastic scaling approaches and mechanisms
  • Data governance and privacy of “data in motion” and “data at rest” over edge/cloud
  • Emerging Data deployment models in IoT, IoT-to-Cloud, Edge/fog
  • Federated Learning and edge intelligence for big data systems
  • Advances data storage models, including object stores and key-value stores
  • Techniques for data integrity, availability, reliability, and fault tolerance
  • Big Data workflows (data management, data wrangling, automated workflows)
  • Data pipeline (data lake to analytics, new data stream architectures, edge/fog, cloudenabled solutions)
  • High-performance Data Analytics applications
  • Adaptive offloading techniques among Fog, Edge, and Cloud Computing

Delta Research Centre

Sponsors