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During the last forty years, data management systems have grown in scale, complexity, and number of installations. At the same time, administration of these systems has become very expensive with the human factor dominating the total cost of ownership. Current trends like cloud computing make this situation even more problematic for service providers who have to configure and manage thousands of database nodes.
There has been a significant amount of research addressing this problem by providing autonomic or self-* features in database systems to support complex administrative tasks like physical database design, problem diagnosis, and performance tuning. However, new challenges arise from trends like cloud and cluster computing, virtualization, and Software-as-a-Service (SaaS). A major challenge is the need to scale self-management capabilities to the level of hundreds to thousands of nodes while taking economic factors into account.
Autonomic, or self-managing, systems are a promising approach to achieve the goal of systems that are easier to use and maintain. A system is considered to be autonomic if it possesses the capabilities to be self-configuring, self-optimizing, self-healing and self-protecting. The aim of the SMDB workshop is to provide a forum for researchers from both industry and academia to present and discuss ideas related to self-management and self-organization in data management systems ranging from classical databases to data stream engines to large-scale cloud environments that utilize advanced AI, machine learning, and data mining and analysis.
We plan to follow the successful format of previous instances of this workshop: approximately 10 presentations of accepted papers, a keynote address by a well-known speaker and subject matter expert in self-managing database systems, as well as a panel discussion involving experts from industry and academia.


Download CFP


Topics of interest include, but are not limited to:

* Principles and architecture of autonomic data management systems
* Retro-fitting existing systems vs. designing for self management
* Self-* capabilities in databases and storage systems
* Data management in cloud and multi-tenant databases
* Autonomic capabilities in database-as-a-service platforms
* Automated testing of data management systems
* Automated physical database design and adaptive query tuning
* Automated provisioning and integration
* Automatic enforcement of information quality
* Robust query processing techniques
* Self-managing data stream engines and adaptive event-based systems
* Self-managing distributed / decentralized / peer-to-peer information systems
* Self-management of internet-scale distributed systems
* Self-management for big data infrastructures
* Monitoring and diagnostics in data management systems
* Policy automation and visualization for datacenter administration
* User acceptance and trust of autonomic capabilities
* Evaluation criteria and benchmarks for self-managing systems
* Self-evaluation of data management services in the cloud
* Use cases and war stories on deploying autonomic capabilities


Authors are invited to submit original research contributions in English of up to 6 pages in the IEEE camera-ready format (templates are available at the ICDE 2021 submission guidelines page) to the submission site https://cmt3.research.microsoft.com/SMDB2021. Authors of accepted papers will be encouraged to submit an extended paper of up to 8 pages for final publication. Author are also invited to submit short papers up to 4 pages. The page limit includes the bibliography and any appendix. All accepted papers will appear in the formal Proceedings of the Conference Workshops published by IEEE CS Press, and will be included in the IEEE digital library.

Authors of a selection of accepted papers will be invited to submit an extended version to the Distributed and Parallel Databases (DAPD) journal.


Paper submission deadline:

January 11 January 31, 2021 5pm PST (abstract) (optional)
January 18 January 31, 2021 5pm PST


February 15, 2021


March 1, 2021



Panos K. Chrysanthis

Professor, Computer Science Department
University of Pittsburgh

Meichun Hsu

Sr. Director of R&D Database Server Technology
Oracle Corporation

Herodotos Herodotou

Assistant Professor, Dept. of Electrical Eng., Computer Eng. and Informatics
Cyprus University of Technology

Yingjun Wu

Software Development Engineer, Redshift Team
Amazon Web Services

Constantinos Costa

Research Associate, Computer Science Department
University of Pittsburgh


  • Alkis Simitsis, Athena Research Center, Greece
  • Andreas Kipf, Massachusetts Institute of Technology, USA
  • Bailu Ding, Microsoft Research, USA
  • Deepak Majeti, Vertica/MicroFocus, USA
  • Eduardo Cunha de Almeida, Federal University of Paraná, Brazil
  • Evaggelia Pitoura, U. Ioannina, Greece
  • George Pallis, University of Cyprus, Cyprus
  • Guoliang Li, Tsinghua University, China
  • Jiaheng Lu, University of Helsinki, Finland
  • Kai-Uwe Sattler, TU Ilmenau, Germany
  • Ken Salem, University of Waterloo, Canada
  • Khuzaima Daudjee, University of Waterloo, Canada
  • Le Gruenwald, University of Oklahoma, USA
  • Matthias J Sax, Confluent Inc., USA
  • Mohamed A Sharaf, United Arab Emirates University, UAE
  • Nesime Tatbul, Intel Labs and MIT, USA
  • Nikos Katsipoulakis, Amazon Web Services, USA
  • Peter Triantafillou, University of Warwick, UK
  • Rebecca Taft, Cockroach Labs, USA
  • Ryan Marcus, MIT, USA
  • Uta Störl, Darmstadt University of Applied Sciences, Germany
  • Vivek Narasayya, Microsoft Research, USA
  • Yao Lu, Microsoft Research, USA


Constantinos Costa, University of Pittsburgh, USA


Brian Nixon, University of Pittsburgh, USA

Rakan Alseghayer, University of Pittsburgh, USA