Scientific workflows in data analysis: Bridging expertise across multiple domains
Scientific workflows in data analysis: Bridging expertise across multiple domains
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DOI：
10.1016/j.future.2017.01.001
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发表时间：
2017-10-01
影响因子：
7.5
通讯作者：
Gil，Yolanda
中科院分区：
文献类型：
--
作者：
Sethi，Ricky J.;Gil，Yolanda
在本文中，我们通过在文本分析、图像分析和视频活动分析领域重新利用工作流程片段，展示了科学工作流程在跨多个领域桥接专业知识方面的用途。我们强调工作流程的重用如何使科学家能够跨学科联系并利用超出其正常专业领域的跨学科研究的好处。此外，我们还对各种任务进行了深入研究，包括文本分析任务、涉及图像和文本的多媒体分析、视频活动分析以及使用深度学习的艺术风格分析。这些任务展示了工作流程片段的重用如何将预先存在的基本方法转变为专家级分析。我们还研究工作流程片段如何节省时间和精力，同时融合机器学习和计算机视觉等多个领域的专业知识。
In this paper, we demonstrate the use of scientific workflows in bridging expertise across multiple domains by re-purposing workflow fragments in the areas of text analysis, image analysis, and analysis of activity in video. We highlight how the reuse of workflows allows scientists to link across disciplines and avail themselves of the benefits of inter-disciplinary research beyond their normal area of expertise. In addition, we present in-depth studies of various tasks, including tasks for text analysis, multimedia analysis involving both images and text, video activity analysis, and analysis of artistic style using deep learning. These tasks show how the re-use of workflow fragments can turn a pre-existing, rudimentary approach into an expert-grade analysis. We also examine how workflow fragments save time and effort while amalgamating expertise in multiple areas such as machine learning and computer vision.