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

被引：4

作者：

Zhang, Jia ^{[1
]}

Wang, Wei ^{[1
]}

Wei, Xing ^{[1
]}

Lee, Chris ^{[1
]}

Lee, Seungwon ^{[2
]}

Pan, Lei ^{[2
]}

Lee, Tsengdar J. ^{[3
]}

机构：

[1] Carnegie Mellon Univ Silicon Valley, Moffett Field, CA 94035 USA

[2] CALTECH, Jet Prop Lab, Pasadena, CA 91125 USA

[3] NASA Headquarters, Sci Miss Directorate, Washington, DC USA

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS) | 2015年

基金：

美国国家科学基金会;

关键词：

Service recommendation; automatic workflow generation;

D O I：

10.1109/ICWS.2015.22

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：89 / 97

页数：9