Topic 5: Parallel and Distributed Data Management


The advent of large and heterogeneous data sets poses a complex hierarchy of requirements ranging from the integration and management of data to complex data analytics. In addition, managing varied data requires heterogeneous solutions that integrate several data management paradigms. Thus, data intensive applications require new approaches and efficient techniques to perform such tasks on the locally stored or geographically dispersed data to cope with this data explosion and heterogeneity.

Another crucial issue is the design of highly scalable distributed data platforms offering consistency levels and programming models capable of simplifying the development of complex, big-data applications, with the ultimate goal of sheltering programmers from sources of complexity like concurrency, distribution and failures.

Beyond that, it is still necessary to improve the provisioning, staging, manipulation, continuous maintenance and monitoring of data hosted in autonomous, distributed and heterogeneous systems. The issue of self-tuning is also of paramount importance in distributed cloud data platforms, which aim to minimize the infrastructure's operational costs by elastically adapting their scale to match dynamic shifts of the workload. Interestingly, these problems can be approached using inter-disciplinary methodologies, such as machine learning, analytical modelling, and control theory.

The parallel and concurrent execution at all levels remains key to enable the development of scalable and effective data intensive applications, which is also affected by enhanced capacities and extended functionalities of the IT infrastructures. This topic seeks papers in all aspects of distributed and parallel data management and data intensive applications, which are focused around the notions of concurrency, parallelism and distributed processing.


  • Parallel, replicated, and highly-available distributed databases
  • Data-intensive clouds and grids
  • Middleware for processing large-scale data
  • Distributed and parallel transaction and query processing over
  • omogeneous and heterogeneous management paradigms
  • Management of parallel and distributed data sources
  • Integration of large datasets on parallel systems
  • Internet-scale data-intensive applications
  • Sensor-network data management
  • Mobile data management
  • Parallel and distributed information retrieval
  • Data-intensive peer-to-peer systems
  • Distributed and cloud-based storage architectures and file systems
  • Parallel data streaming and data stream mining
  • NoSQL data management and analysis: key value, graph management, etc.
  • Algorithms for security and privacy in data management
  • Parallel and distributed knowledge discovery and data mining

Topic Committee

Global Chair
Josep L. Larriba-Pey, Polytechnic University of Catalonia, Spain

Local Chair
Paolo Romano, IST-University of Lisbon, Portugal

Further Members
Kai-Uwe Sattler, Technical University of Ilmenau, Germany
Yang-Sae Moon, Kangwon National University, Korea
Patrick Martin, Queen's University, Kingston, Canada
David Dominguez-Sal, Sparsity Technologies, Spain