A Survey of Data-Intensive Scientific Workflow Management <s> Scientific Workflow Life Cycle <s> Scientific workflow systems have become a necessary tool for many applications, enabling the composition and execution of complex analysis on distributed resources. Today there are many workflow systems, often with overlapping functionality. A key issue for potential users of workflow systems is the need to be able to compare the capabilities of the various available tools. There can be confusion about system functionality and the tools are often selected without a proper functional analysis. In this paper we extract a taxonomy of features from the way scientists make use of existing workflow systems and we illustrate this feature set by providing some examples taken from existing workflow systems. The taxonomy provides end users with a mechanism by which they can assess the suitability of workflow in general and how they might use these features to make an informed choice about which workflow system would be a good choice for their particular application. <s> BIB001 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Scientific Workflow Life Cycle <s> The problem of systematically capturing and managing provenance for computational tasks has recently received significant attention because of its relevance to a wide range of domains and applications. The authors give an overview of important concepts related to provenance management, so that potential users can make informed decisions when selecting or designing a provenance solution. <s> BIB002 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Scientific Workflow Life Cycle <s> One of the main challenges of scientific experiments is to allow scientists to manage and exchange their scientific computational resources (data, programs, models, etc.). The effective management of such experiments requires a specific set of cardinal facilities, such as experiment specification techniques, workflow derivation heuristics and provenance mechanisms. These facilities may characterise the experiment life cycle into three phases: composition, execution, and analysis. Works concerned with supporting scientific workflows are mainly concerned with the execution and analysis phase. Therefore, they fail to support the scientific experiment throughout its life cycle as a set of integrated experimentation technologies. In large scale experiments this represents a research challenge. We propose an approach for managing large scale experiments based on provenance gathering during all phases of the life cycle. We foresee that such approach may aid scientists to have more control on the trials of the scientific experiment. <s> BIB003 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Scientific Workflow Life Cycle <s> Abstract Scientific workflows have emerged as an important tool for combining the computational power with data analysis for all scientific domains in e-science, especially in the life sciences. They help scientists to design and execute complex in silico experiments. However, with rising complexity it becomes increasingly impractical to optimize scientific workflows by trial and error. To address this issue, we propose to insert a new optimization phase into the common scientific workflow life cycle. This paper describes the design and implementation of an automated optimization framework for scientific workflows to implement this phase. Our framework was integrated into Taverna, a life-science oriented workflow management system and offers a versatile programming interface (API), which enables easy integration of arbitrary optimization methods. We have used this API to develop an example plugin for parameter optimization that is based on a Genetic Algorithm. Two use cases taken from the areas of structural bioinformatics and proteomics demonstrate how our framework facilitates setup, execution, and monitoring of workflow parameter optimization in high performance computing e-science environments. <s> BIB004 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Scientific Workflow Life Cycle <s> WS-PGRADE/gUSE is a gateway framework that offers a set of high-level grid and cloud services by which interoperation between grids, clouds, and scientific user communities can be achieved. gUSE is also a workflow system that enables scientific communities to compose and execute a series of computational or data manipulation steps in a scientific application on Distributed Computing Infrastructures (DCIs). The current chapter summarizes the most important features of WS-PGRADE/gUSE. <s> BIB005 </s> A Survey of Data-Intensive Scientific Workflow Management <s> Scientific Workflow Life Cycle <s> E-scientists want to run their scientific experiments on Distributed Computing Infrastructures (DCI) to be able to access large pools of resources and services. To run experiments on these infrastructures requires specific expertise that e-scientists may not have. Workflows can hide resources and services as a virtualization layer providing a user interface that e-scientists can use. There are many workflow systems used by research communities but they are not interoperable. To learn a workflow system and create workflows in this workflow system may require significant efforts from e-scientists. Considering these efforts it is not reasonable to expect that research communities will learn new workflow systems if they want to run workflows developed in other workflow systems. The solution is to create workflow interoperability solutions to allow workflow sharing. The FP7 Sharing Interoperable Workflow for Large-Scale Scientific Simulation on Available DCIs (SHIWA) project developed two interoperability solutions to support workflow sharing: Coarse-Grained Interoperability (CGI) and Fine-Grained Interoperability (FGI). The project created the SHIWA Simulation Platform (SSP) to implement the Coarse-Grained Interoperability approach as a production-level service for research communities. The paper describes the CGI approach and how it enables sharing and combining existing workflows into complex applications and run them on Distributed Computing Infrastructures. The paper also outlines the architecture, components and usage scenarios of the simulation platform. <s> BIB006
The life cycle of a scientific workflow is a description of the state transitions of a scientific workflow from creation to completion BIB001  . A scientific workflow life cycle generally contains four phases. Görlach et al.  propose that a scientific workflow life cycle contains modeling phase, deployment phase, execution and monitoring phase, and analysis phase. Deelman et al. BIB001 argue that a scientific workflow life cycle consists of composition phase, mapping phase, execution phase and provenance phase. Provenance data represents information regarding workflow execution BIB002 . We present provenance in more details in the next section. In BIB003 , a provenance database is proposed to represent and relate data from several phases of the workflow life cycle. In this paper, we adopt a combination of workflow life cycle views BIB001  BIB003 with a few variations, condensed in four phases: 1. The composition phase BIB001 BIB003 is for the creation of an abstract scientific workflow. An abstract scientific workflow is defined by the functionality of each activity (or sub-workflow) and data dependencies between activities (or sub-workflows) BIB005 BIB006 . Workflow composition can be done through a textual or Graphical User Interface (GUI). SWfMS users can reuse the existing scientific workflows with or without modification BIB004 . 2. The deployment phase  is for constructing a concrete scientific workflow, which consists of concrete methods (and associated codes) for each functional activity (or sub-workflow) defined in the workflow composition phase, so that the workflow can be executed. 3. The execution phase BIB001 BIB003 is for the execution of scientific workflows with associated data, during which input data is processed and output data is produced. 4. The analysis phase  BIB003 is to apply the output data to scientific experiments, to analyze workflow provenance data and to share the workflow information.