Topic 5: Parallel and Distributed Data Management

摘要

The ever-increasing data volumes used to empower contemporary data-intensive applications as well as aggregations of computing systems call for novel approaches and efficient techniques in the management of geographically dispersed data. Despite recent advances, Internet-scale requirements for both applications and underlying systems require effective provisioning, staging, manipulation, continuous maintenance and monitoring of data hosted in multiple, pre-existing autonomous, distributed and often heterogeneous systems. Evidently, the notions of parallelism and concurrent execution at all levels remain key elements in attaining scalability and effective management for nearly-all modern data-intensive applications. Moreover, as underlying computing environments get transformed through the introduction of novel infrastructures, enhanced capacities and extended functionalities, new solutions are sought to cope with these changes. In topic 5, we solicited papers in all aspects of data management (access, query, and analysis) and data-intensive applications whose central focus is weaved around the notions of concurrency, parallelism and distributed processing. Key areas that were of interest included parallel and highly-available distributed databases, data-intensive clouds, middleware solutions for processing large-scale data, distributed transaction and query processing, management of distributed data sources, Internet-scale applications, parallel and distributed information retrieval, data-intensive peer-to-peer systems efficient management of data streams, scalable web services as well as data analysis on multi-core and many-core architectures.