A Framework for Efficient Data Analytics through Automatic Configuration and Customization of Scientific Workflows

Matheus Hauder · Yolanda Gil · Yan Liu

17 citations0/0 files storedIEEE International Conference on eScience

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Scientific Computing and Data Management Business Process Modeling and Analysis Data Mining Algorithms and Applications

Abstract

Data analytics involves choosing between many different algorithms and experimenting with possible combinations of those algorithms. Existing approaches however do not support scientists with the laborious tasks of exploring the design space of computational experiments. We have developed a framework to assist scientists with data analysis tasks in particular machine learning and data mining. It takes advantage of the unique capabilities of the Wings workflow system to reason about semantic constraints. We show how the framework can rule out invalid workflows and help scientists to explore the design space. We demonstrate our system in the domain of text analytics, and outline the benefits of our approach.

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2005Taverna: lessons in creating a workflow environment for the life sciences 2007Workflows For E-Science: Scientific Workflows For Grids 2006VisTrails 2008The design and realisation of the Virtual Research Environment for social sharing of workflows 2010Wings: Intelligent Workflow-Based Design of Computational Experiments 2011A semantic framework for automatic generation of computational workflows using distributed data and component catalogues 2009Expressive Reusable Workflow Templates 2006Recycling workflows and services through discovery and reuse

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2013Time-bound analytic tasks on large datasets through dynamic configuration of workflows 2017Scientific workflows in data analysis: Bridging expertise across multiple domains 2012Re-Using Workflow Fragments across Multiple Data Domains

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