Abstract. Scientific data and workflow management pose unique computer science research challenges, in particular for the database community. While traditional techniques, developed mostly for commercial/business applications, remain an important ingredient for scientific data and workflow management, it has been recognized that the volume and complexity of data and tasks in scientific application domains require the development of new technologies and tools. The overall goal of this tutorial is to provide an introduction to several important areas of research and development in scientific data and workflow management. Given the breadth of the topic area, the tutorial will inevitably be mostly a high-level introduction to the various subfields in scientific data and workflow management. However, some concepts, techniques, and applications will be presented in more detail. The tutorial is organized into the following modules: We first provide a high-level overview of the challenges and basic techniques of scientific data and workflow management, such as different types of scientific data and metadata, data management processes, scientific workflows, and the role of data provenance and knowledge representation (ontologies). The second module focuses on streaming scientific data that is processed on-line, often in real-time. We present the fundamental models, techniques, and architectures underlying data processing pipelines that deal with different types of streaming scientific data. A particular focus is on spatio-temporal data originating from sensor networks and remote-sensing equipment such as satellites and telescopes. The concept of "scientific workflow" aims at a wide area of applications, ranging from experiment and workflow design to execution, monitoring, archival, and reuse of data management and analysis pipelines. For example, aspects such as automated metadata and provenance management need to be integrated into scientific workflow systems. Such mechanisms facilitate data analysis and interpretation as well as workflow reuse. The tutorial is aimed at researchers and scientists from industry, government, and academia who are interested in management and analysis of large-scale scientific data sets, and who want to get an overview of new models, techniques, and architectures to effectively manage scientific data. We use illustrating examples from different scientific application domains, including life sciences, geosciences, and cosmology.
