SWF 2013 - IEEE 2013 International Workshop on Scientific Workflows (SWF 2013)

Today, many science and engineering disciplines have become increasingly data-intensive. Massively complex new instruments and simulators are generating massive data sets that are described as big data. For example, in Physics, the Large Hadron Collider will eventually generate about 15 petabytes (1 petabye is about 1,000,000 gigabyes) of data per year. In neuroscience, a complete map of the brain's neural circuitry would generate about 1000 exabytes (an exabyte is about 1000 petabytes). The coming data deluge poses great challenges to the whole lifecycle of data management, from data collection, data storage, to data processing and visualization. In the meanwhile, workflow has become a popular paradigm for scientists and engineers to formalize and structure complex processes to solve increasingly data-intensive scientific and engineering problems. The importance of workflow is well recognized by NSF as well as by numerous workshops. As a recent Science article concluded, "In the future, the rapidity with which any given discipline advances is likely to depend on how well the community acquires the necessary expertise in database, workflow management, visualization, and cloud computing technologies."
The theme of this year's SWF workshop is "Advances in Data and Event-Driven Workflows", recognizing the data-centric and event-driven approach of scientific and engineering workflows, as opposed to the traditional process-centric approach to workflow management. Built upon the successful history of SWF (http://www.cs.wayne.edu/~shiyong/swf/) since 2007, this year, we broaden the scope of SWF to include big data oriented workflows as well, soliciting papers to share the challenges, experiences, and lessons in applying workflow technologies to various data-driven science and engineering problems. Topics of interests include, but are not limited to:
List of topics
? Data-driven workflows
? Event-driven workflows
? Scientific workflow provenance management and analytics
? Scientific workflow data, metadata, service, and task management
? Scientific workflow architectures, models, languages, systems, and algorithms
? Scientific workflow monitoring, debugging, exception handling, and fault tolerance
? Streaming data processing in scientific workflows
? Pipelined, data, workflow, and task parallelism in scientific workflows
? Cloud, Service, Grid, or hybrid scientific workflows
? Data, metadata, compute, user-interaction, or visualization-intensive scientific workflows
? Semantic techniques for scientific workflows
? Scientific workflow composition
? Security issues in scientific workflows
? Data integration and service integration in scientific workflows
? Scientific workflow mapping, optimization, and scheduling
? Scientific workflow modeling, simulation, analysis, and verification
? Scalability, reliability, extensibility, agility, and interoperability
? Scientific workflow applications and case studies
? Enterprise workflow management and services computing
? Enterprise workflow cooperation and collaboration