Workshop Description
During the last forty years, data management systems have grown in scale, complexity, and number of installations, while the workloads they serve have become more diverse and demanding. Current trends like cloud computing make this situation even more challenging for service providers who have to configure and manage thousands of database nodes as well as to ensure that service level agreements are met.
There has been a significant amount of research addressing these issues by providing autonomic or self-* features in database systems to support complex administrative tasks, such as physical database design, problem diagnosis, and performance tuning, as well as to optimize the operations of database components such as the query optimizer and the execution engine. 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 considering economic factors.
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 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.
Message from Chairs
Timetable
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