Recently updated on: 2019-11-12
Similarity Assessment for Scientific Workflow Clustering and Recommendation
2018–12–14
Mingfa Pearl Spring Hotel
报告内容：
With an increasing number of scientific workflows published at online repositories and publicly accessible through the Web, reuse and repurposing of best practices is of core importance when a novel scientific workflow is required to be developed for satisfying the requirement of scientists. To address this challenge, this article proposes an approach for identifying and recommending workflows for reference. Specifically, a scientific workflow is represented as a layer hierarchy, which specifies hierarchical relations between this workflow, its sub-workflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows. A graph-skeleton based clustering technique is adopted for grouping layer hierarchies into clusters. Barycenters in each cluster are identified, which refer to core workflows in this cluster, for facilitating cluster identification and workflow ranking and recommendation. Experimental evaluation shows that our technique is efficient and accurate on ranking and recommending appropriate clusters and workflows.
个人简介：
周长兵现为中国地质大学(北京)信息工程学院教授、博士生导师，学校学术委员会委员，法国国立电信学院南巴黎分校计算机系客座教授。曾在法国国立电信大学南巴黎分校计算机系从事博士后研究工作，博士毕业于DERI Galway 爱尔兰，硕士毕业于中国科学院自动化研究所，本科毕业于中国地质大学(武汉)。曾在朗讯科技贝尔实验室(中国)工作过5 年，任高级研究员职位；曾在华为公司北京研究所工作担任软件工程师职位。研究兴趣包括软件服务工程、物联网技术、业务流程管理等，在IEEE Transactions on Services Computing、IEEE Transactions on Industrial Informatics、IEEE ICWS等知名国际期刊和国际会议上发表学术论文100余篇。