Small Is Beautiful: Summarizing Scientific Workflows Using Semantic Annotations
Small Is Beautiful: Summarizing Scientific Workflows Using Semantic Annotations
复制标题
DOI：
10.1109/bigdata.congress.2013.49
复制
发表时间：
2013-06
期刊：
影响因子：
--
通讯作者：
Pinar Alper;Khalid Belhajjame;C. Goble;Pinar Senkul
中科院分区：
文献类型：
--
作者：
Pinar Alper;Khalid Belhajjame;C. Goble;Pinar Senkul
Scientific workflows have become the workhorse of Big Data analytics for scientists. As well as being repeatable and optimizable pipelines that bring together datasets and analysis tools, workflows make-up an important part of the provenance of data generated from their execution. By faithfully capturing all stages in the analysis, workflows play a critical part in building up the audit-trail (a.k.a. provenance) meta-data for derived datasets and contributes to the veracity of results. Provenance is essential for reporting results, reporting the method followed, and adapting to changes in the datasets or tools. These functions, however, are hampered by the complexity of workflows and consequently the complexity of data-trails generated from their instrumented execution. In this paper we propose the generation of workflow description summaries in order to tackle workflow complexity. We elaborate reduction primitives for summarizing workflows, and show how primitives, as building blocks, can be used in conjunction with semantic workflow annotations to encode different summarization strategies. We report on the effectiveness of the method through experimental evaluation using real-world workflows from the Tavern a system.
