Climate Analytics Workflow Recommendation as a Service - Provenance-driven Automatic Workflow Mashup

Existing scientific workflow tools, created by computer scientists, require that domain scientists meticulously design their multi-step experiments before analyzing data. However, this is oftentimes contradictory to a domain scientist's routine of conducting research and exploration. This paper presents a novel way to resolve this dispute, in the context of service-oriented science. After scrutinizing how Earth scientists conduct data analytics research in their daily work, a provenance model is developed to record their activities. Reverse-engineering the provenance, a technology is developed to automatically generate workflows for scientists to review and revise, supported by a Petri nets-based workflow verification instrument. In addition, dataset is proposed to be treated as first-class citizen to drive the knowledge sharing and recommendation. A data-centric repository infrastructure is established to catch richer provenance to further facilitate collaboration in the science community. In this way, we aim to revolutionize computer-supported Earth science.