Introduction to Topic 5: Parallel and Distributed Data Management

The manipulation and handling of an ever increasing volume of data by current data-intensive applications require novel techniques for efficient data management. Despite recent advances in every aspect of data management (storage, access, querying, analysis, mining), future applications are expected to scale to even higher degrees, not only in terms of volumes of data handled but also in terms of users and resources, often making use of multiple, pre-existing autonomous, distributed or heterogeneous resources. The notion of parallelism and concurrent execution at all levels remains a key element in achieving scalability and managing efficiently such data-intensive applications, but the changing nature of the underlying environments requires new solutions to cope with such changes. In this context, this topic sought papers in all aspects of data management (including databases and data-intensive applications) that focus on some form of parallelism and concurrency. Each paper was reviewed by four reviewers and, after discussion, we were able to select four regular papers. The accepted papers address relevant issues on various topics such as effective data compression, GPU-based data indexing, distributed collaborative data filtering and parallel query processing.