Scientific Workflow Systems Research Articles

Enriching Agronomic Experiments with Data Provenance

Reproducibility is a major feature of Science. Even agronomic research of exemplary quality may have irreproducible empirical findings because of random or systematic error. The ability to reproduce agronomic experiments based on statistical data and legacy scripts are not easily achieved. We propose RFlow, a tool that aid researchers to manage, share, and enact the scientific experiments that encapsulate legacy R scripts. RFlow transparently captures provenance of scripts and endows experiments reproducibility. Unlike existing computational approaches, RFlow is non-intrusive, does not require users to change their working way, it wraps agronomic experiments in a scientific workflow system. Our computational experiments show that the tool can collect different types of provenance metadata of real experiments and enrich agronomic data with provenance metadata. This study shows the potential of RFlow to serve as the primary integration platform for legacy R scripts, with implications for other data- and compute-intensive agronomic projects.

Based on Digital Archives for Scientific Workflow System Active Service

This paper describes the architecture of active service model of scientific workflow system. Digital archives technologies are available for all the resources needed to construct services specific group. This paper reviews the introduction and motivation for active service approach, describes the architecture of scientific workflow system, discusses the technologies used in active service, which uses digital archives technology to enable multi-service organizations achieve scientific workflow goals.

Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities

With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance.The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of reproducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments.

Raw data queries during data-intensive parallel workflow execution

Computer simulations consume and produce huge amounts of raw data files presented in different formats, e.g., HDF5 in computational fluid dynamics simulations. Users often need to analyze domain-specific data based on related data elements from multiple files during the execution of computer simulations. In a raw data analysis, one should identify regions of interest in the data space and retrieve the content of specific related raw data files. Existing solutions, such as FastBit and RAW, are limited to a single raw data file analysis and can only be used after the execution of computer simulations. Scientific Workflow Management Systems (SWMS) can manage the dataflow of computer simulations and register related raw data files at a provenance database. This paper aims to combine the advantages of a dataflow-aware SWMS and the raw data file analysis techniques to allow for queries on raw data file elements that are related, but reside in separate files. We propose a component-based architecture, named as ARMFUL (Analysis of Raw data from Multiple Files) with raw data extraction and indexing techniques, which allows for a direct access to specific elements or regions of raw data space. ARMFUL innovates by using a SWMS provenance database to add a dataflow access path to raw data files. ARMFUL facilitates the invocation of ad-hoc programs and third party tools (e.g., FastBit tool) for raw data analyses. In our experiments, a real parallel computational fluid dynamics is executed, exploring different alternatives of raw data extraction, indexing and analysis.

Thermo-hydro-mechanical model parameters adjustment via a distributed scientific workflow system

Este trabajo presenta una estrategia numérica para el ajuste automático de parámetros del Modelo de Daño de Mazars, aplicado a un modelado termohidro- mecánico de estructuras de concreto. El procedimiento se basa en un sistema de flujo de trabajo científico (SWS) que aborda el universo combinatorio de parámetros ajustables al minimizar el número de simulaciones requeridas para obtener resultados optimizados. SWS no sólo mejora la eficiencia, sino que también facilita la estrategia en comparación con los procedimientos manuales. El algoritmo adoptado se desarrolla en un lenguaje de escritura intuitivo y emplea un ambiente computacional distribuido. La comparación con los datos experimentales indica que la metodología propuesta fue eficiente y efectiva para mejorar el análisis, minimizando los errores y ahorrando tiempo de procesamiento.

Facilitating the Reproducibility of Scientific Workflows with Execution Environment Specifications

Scientific workflows are designed to solve complex scientific problems and accelerate scientific progress. Ideally, scientific workflows should improve the reproducibility of scientific applications by making it easier to share and reuse workflows between scientists. However, scientists often find it difficult to reuse others’ workflows, which is known as workflow decay. In this paper, we explore the challenges in reproducing scientific workflows, and propose a framework for facilitating the reproducibility of scientific workflows at the task level by giving scientists complete control over the execution environments of the tasks in their workflows and integrating execution environment specifications into scientific workflow systems. Our framework allows dependencies to be archived in basic units of OS image, software and data instead of gigantic all-in-one images. We implement a prototype of our framework by integrating Umbrella, an execution environment creator, into Makeflow, a scientific workflow system.To evaluate our framework, we use it to run two bioinformatics scientific workflows, BLAST and BWA. The execution environment of the tasks in each workflow is specified as an Umbrella specification file, and sent to execution nodes where Umbrella is used to create the specified environment for running the tasks. For each workflow we evaluate the size of the Umbrella specification file, the time and space overheads of creating execution environments using Umbrella, and the heterogeneity of execution nodes contributing to each workflow. The evaluation results show that our framework improves the utilization of heterogeneous computing resources, and improves the portability and reproducibility of scientific workflows.

Porting HPC applications to the cloud: A multi-frontal solver case study

In this paper we argue that scientific applications traditionally considered as representing typical HPC workloads can be successfully and efficiently ported to a cloud infrastructure. We propose a porting methodology that enables parallelization of communication – and memory-intensive applications while achieving a good communication to computation ratio and a satisfactory performance in a cloud infrastructure. This methodology comprises several aspects: (1) task agglomeration heuristic enabling increasing granularity of tasks while ensuring they will fit in memory; (2) task scheduling heuristic increasing data locality; and (3) two-level storage architecture enabling in-memory storage of intermediate data. We implement this methodology in a scientific workflow system and use it to parallelize a multi-frontal solver for finite-element meshes, deploy it in a cloud, and execute it as a workflow. The results obtained from the experiments confirm that the proposed porting methodology leads to a significant reduction of communication costs and achievement of a satisfactory performance. We believe that these results constitute a valuable step toward a wider adoption of cloud infrastructures for computational science applications.

Improved CTT-SP Algorithm with Critical Path Method for Massive Data Storage in Scientific Workflow Systems

Scientific WorkFlows (SWFs) play significant roles in scientific research and engineering simulation, which are often data intensive and has complex data dependencies. The storage of massive intermediate datasets has great impacts on the performance and the quality of service of a SWF system, which has become a difficult and complex task. Through analyzing the cost transitive tournament shortest path (CTT-SP)-based algorithm proposed for the intermediate data storage in SWF systems, the disadvantage of the CTT-SP algorithm over its sensitivity to the main branches can be found. We proposed an improved CTT-SP algorithm based on the critical path method (CPM), aiming at reducing the sensitivity of the main branch and improving the performance of the algorithm. By adding the generation information of the datasets on the arrows, the data dependency graph of a SWF can be converted to an activity on arrow (AOA) net. Then, the critical path of the AOA net is used to be the main branch of the CTT-SP algorithm, which can reduce the impacts of the main branch and keep the quality of service of the SWF systems. Experiments are designed to test the impacts of the main branches and the performance of the improved CTT-SP algorithms. The results show the significant impacts of the main branches on the performance of the CTT-SP algorithm and the effectiveness of the CPM on improving the performance of the CTT-SP algorithm. Comparison results demonstrate the positiveness and effectiveness of the improved CTT-SP algorithm.

NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig.

The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This “neuroscience Big data” represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks. However, many existing neuroinformatics data processing and analysis tools were not built to manage large volume of data, which makes it difficult for researchers to effectively leverage this available data to advance their research. We introduce a new toolkit called NeuroPigPen that was developed using Apache Hadoop and Pig data flow language to address the challenges posed by large-scale electrophysiological signal data. NeuroPigPen is a modular toolkit that can process large volumes of electrophysiological signal data, such as Electroencephalogram (EEG), Electrocardiogram (ECG), and blood oxygen levels (SpO2), using a new distributed storage model called Cloudwave Signal Format (CSF) that supports easy partitioning and storage of signal data on commodity hardware. NeuroPigPen was developed with three design principles: (a) Scalability—the ability to efficiently process increasing volumes of data; (b) Adaptability—the toolkit can be deployed across different computing configurations; and (c) Ease of programming—the toolkit can be easily used to compose multi-step data processing pipelines using high-level programming constructs. The NeuroPigPen toolkit was evaluated using 750 GB of electrophysiological signal data over a variety of Hadoop cluster configurations ranging from 3 to 30 Data nodes. The evaluation results demonstrate that the toolkit is highly scalable and adaptable, which makes it suitable for use in neuroscience applications as a scalable data processing toolkit. As part of the ongoing extension of NeuroPigPen, we are developing new modules to support statistical functions to analyze signal data for brain connectivity research. In addition, the toolkit is being extended to allow integration with scientific workflow systems. NeuroPigPen is released under BSD license at: https://sites.google.com/a/case.edu/neuropigpen/.

A Dynamic Cloud Dimensioning Approach for Parallel Scientific Workflows: a Case Study in the Comparative Genomics Domain

Usually, scientists need to execute experiments that demand high performance computing environments and parallel techniques. This is the scenario found in many bioinformatics experiments modeled as scientific workflows, such as phylogenetic and phylogenomic analyses. To execute these experiments, scientists have adopted virtual machines (VMs) instantiated in clouds. Estimating the number of VMs to instantiate is a crucial task to avoid negative impacts on the execution performance and on the financial costs with under or overestimations. Previously, the necessary number of VMs to execute bioinformatics workflows have been estimated by a GRASP heuristic and have been coupled to a Cloud-based Parallel Scientific Workflow Management System. Although this work was a step forward, this approach only provided a static dimensioning. If the characteristics of the environment change (processing capacity, network speed), this static dimensioning may not be suitable. In this way, it is of interest that the dimensioning is adjusted at runtime. To achieve this, we developed a novel framework for monitoring and dynamically dimensioning resources during the execution of parallel scientific workflows in clouds, called Dynamic Dimensioning of Cloud Computing Framework (DDC-F). We have evaluated DDC-F in real executions of bioinformatics workflows. Experiments showed that DDC-F is able to efficiently calculate the number of VMs necessary to execute bioinformatics workflows of Comparative Genomics (CG), also reducing the financial costs, when compared with other works of the related literature.

WISP: A pattern‐based approach to the interchange of scientific workflow specifications

SummaryScientific workflows are used for dealing with complex problems in different e‐science domains. These workflows are modeled and executed using Scientific Workflow Management Systems (SWfMSs). Generally, SWfMSs provide their own Workflow Specification Language, and this is a challenge considering the possibility of interchanging workflow specifications between different SWfMSs. In this paper, we propose the use of workflow patterns combined with software architecture concepts to capture the key semantics expressed in scientific workflows specified in different Workflow Specification Languages. These concepts are materialized in a novel specification interchange language (and associated tooling) called Workflow Interchange in Science with Patterns (WISP), which is the main contribution of our work. We have evaluated WISP based on real world specifications; in this paper, we present the results of such evaluation (including an assessment of the quality of the research material available), which has eventually led us to reformulate our initial proposal with more stringent assumptions on the input and output specifications. Copyright © 2016 John Wiley & Sons, Ltd.

A rule-based agent-oriented approach for supporting weakly-structured scientific workflows

Weakly-structured Scientific Workflows (WsSWFs) often contain goal-oriented tasks that are logical and complicated, but they are vital for workflow results. They may involve interactions between multiple participants or have complicated logic to express scientific policies and cater to dynamic execution environments. In general, such WsSWFs not only need a rich process and (domain-specific) decision logic specification, but also require a flexible execution and human interaction. In this paper, we propose a Rule-based Agent-oriented Framework (RbAF) to support the WsSWF execution by combining rule-based knowledge representation with agent technology. We describe workflows by messaging reaction rules, which go beyond global Event-Condition-Action (ECA) rules and support performing complex actions locally within certain contexts. We describe (domain-specific) decision logic in workflows by exploiting the benefits of both Logic Programming (LP) and Description Logic (DL). Our evaluation results show that, RbAF well supports the WsSWFs and has higher expressive power than other three considered scientific workflow systems.

Kepler WebView: A Lightweight, Portable Framework for Constructing Real-time Web Interfaces of Scientific Workflows

Modern web technologies facilitate the creation of high-quality data visualizations, and rich, interactive components across a wide variety of devices. Scientific workflow systems can greatly benefit from these technologies by giving scientists a better understanding of their data or model leading to new insights. While several projects have enabled web access to scientific workflow systems, they are primarily organized as a large portal server encapsulating the workflow engine. In this vision paper, we propose the design for Kepler WebView, a lightweight framework that integrates web technologies with the Kepler Scientific Workflow System. By embedding a web server in the Kepler process, Kepler WebView enables a wide variety of usage scenarios that would be difficult or impossible using the portal model.

A Rule-Based Agent-Oriented Approach for Supporting Weakly-Structured Scientific Workflows

Weakly-structured Scientific Workflows (WsSWFs) often contain goal-oriented tasks that are logical and complicated, but they are vital for workflow results. They may involve interactions between multiple participants or have complicated logic to express scientific policies and cater to dynamic execution environments. In general, such WsSWFs not only need a rich process and (domain-specific) decision logic specification, but also require a flexible execution and human interaction. In this paper, we propose a Rule-based Agent-oriented Framework (RbAF) to support the WsSWF execution by combining rule-based knowledge representation with agent technology. We describe workflows by messaging reaction rules, which go beyond global Event-Condition-Action (ECA) rules and support performing complex actions locally within certain contexts. We describe (domain-specific) decision logic in workflows by exploiting the benefits of both Logic Programming (LP) and Description Logic (DL). Our evaluation results show that, RbAF well supports the WsSWFs and has higher expressive power than other three considered scientific workflow systems.

Integrated Machine Learning in the Kepler Scientific Workflow System

We present a method to integrate multiple implementations of a machine learning algorithm in Kepler actors. This feature enables the user to compare accuracy and scalability of various implementations of a machine learning technique without having to change the workflow. These actors are based on the Execution Choice actor. They can be incorporated into any workflow to provide machine learning functionality. We describe a use case where actors that provide several implementations of k-means clustering can be used in a workflow to process sensor data from weather stations for predicting wildfire risks.

Assessing Run-time Overhead of Securing Kepler

We have developed a model for securing data-flow based application chains. We have imple- mented the model in the form of an add-on package for the scientific workflow system called Kepler. Our Security Analysis Package (SAP) leverages Kepler's Provenance Recorder (PR). SAP secures data flows from external input-based attacks, from access to unauthorized exter- nal sites, and from data integrity issues. It is not a surprise that cost of real-time security is a certain amount of run-time overhead. About half of the overhead appears to come from the use of the Kepler PR and the other half from security function added by SAP.

Scalable Scientific Workflows Management System SWFMS

In today’s electronic world conducting scientific experiments, especially in natural sciences domain, has become more and more challenging for domain scientists since “science” today has turned out to be more complex due to the two dimensional intricacy; one: assorted as well as complex computational (analytical) applications and two: increasingly large volume as well as heterogeneity of scientific data products processed by these applications. Furthermore, the involvement of increasingly large number of scientific instruments such as sensors and machines makes the scientific data management even more challenging since the data generated from such type of instruments are highly complex. To reduce the amount of complexities in conducting scientific experiments as much as possible, an integrated framework that transparently implements the conceptual separation between both the dimensions is direly needed. In order to facilitate scientific experiments ‘workflow’ technology has in recent years emerged in scientific disciplines like biology, bioinformatics, geology, environmental science, and eco-informatics. Much more research work has been done to develop the scientific workflow systems. However, our analysis over these existing systems shows that they lack a well-structured conceptual modeling methodology to deal with the two complex dimensions in a transparent manner. This paper presents a scientific workflow framework that properly addresses these two dimensional complexities in a proper manner.

A Service Framework for Scientific Workflow Management in the Cloud

Cloud computing is an emerging computing paradigm that can offer unprecedented scalability and resources on demand, and is getting more and more adoption in the science community, while scientific workflow management systems provide essential support such as management of data and task dependencies, job scheduling and execution, provenance tracking, etc., to scientific computing. As we are entering into a “big data” era, it is imperative to migrate scientific workflow management systems into the cloud to manage the ever increasing data scale and analysis complexity. We propose a reference service framework for integrating scientific workflow management systems into various cloud platforms, which consists of eight major components, including Cloud Workflow Management Service, Cloud Resource Manager, etc., and six interfaces between them. We also present a reference framework for the implementation of Cloud Resource Manager, which is responsible for the provisioning and management of virtual resources in the cloud. We discuss our implementation of the framework by integrating the Swift scientific workflow management system with theOpenNebula and Eucalyptus cloud platforms, and demonstrate the capability of the solution using a NASA MODIS image processing workflow and a production deployment on the Science@Guoshi network with support for the Montage image mosaic workflow.

Improving workflow design by mining reusable tasks

With the increasing popularity of scientific workflow management systems (SWfMS), more and more workflow specifications are becoming available. Such specifications contain precious knowledge that can be reused to produce new workflows. It is a fact that provenance data can help reusing third party code. However, finding the dependencies among programs without the support of a tool is not a trivial activity and, in many cases, becomes a barrier to build more sophisticated models and analysis. Due to the huge number of task versions available and their configuration parameters, this activity is highly error prone and counterproductive. In this work, we propose workflow recommender (WR), a recommendation service that aims at suggesting frequent combinations of workflow tasks for reuse. It works similarly to an e-commerce application that applies data mining techniques to help users find items they would like to purchase, predicting a list based on other user’s choices. Our experiments show that our approach is effective both in terms of performance and precision of the results. The approach is general in the sense that it can be coupled to any SWfMS.

Scientific workflows for process mining: building blocks, scenarios, and implementation

Over the past decade process mining has emerged as a new analytical discipline able to answer a variety of questions based on event data. Event logs have a very particular structure; events have timestamps, refer to activities and resources, and need to be correlated to form process instances. Process mining results tend to be very different from classical data mining results, e.g., process discovery may yield end-to-end process models capturing different perspectives rather than decision trees or frequent patterns. A process-mining tool like ProM provides hundreds of different process mining techniques ranging from discovery and conformance checking to filtering and prediction. Typically, a combination of techniques is needed and, for every step, there are different techniques that may be very sensitive to parameter settings. Moreover, event logs may be huge and may need to be decomposed and distributed for analysis. These aspects make it very cumbersome to analyze event logs manually. Process mining should be repeatable and automated. Therefore, we propose a framework to support the analysis of process mining workflows. Existing scientific workflow systems and data mining tools are not tailored towards process mining and the artifacts used for analysis (process models and event logs). This paper structures the basic building blocks needed for process mining and describes various analysis scenarios. Based on these requirements we implemented RapidProM, a tool supporting scientific workflows for process mining. Examples illustrating the different scenarios are provided to show the feasibility of the approach.