GExpLine: A Tool for Supporting Experiment Composition

Oliveira, Daniel de; Ogasawara, Eduardo; Seabra, Fernando; Silva, Vítor; Murta, Leonardo; Mattoso, Marta

doi:10.1007/978-3-642-17819-1_28

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Some of them focus on workflow reuse by providing composition at a high abstraction level [12,18,20,22], automatically generating the executable workflow. Others generate the workflow from execution trace logs and retrospective provenance [33][34][35].…”

Section: Our Previous Approachmentioning

confidence: 99%

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Improving workflow design by mining reusable tasks

Tosta¹,

Braganholo

Murta

et al. 2015

J Braz Comput Soc

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Background: 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. Methods: 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. Results: Our experiments show that our approach is effective both in terms of performance and precision of the results. Conclusions:The approach is general in the sense that it can be coupled to any SWfMS.

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Section: Our Previous Approachmentioning

confidence: 99%

“…However, they do not deal with the problem of suggesting new tasks to the user during workflow design. Other existing works [4,[11][12][13][14][15][16][17][18][19][20][21][22][23] propose the use of recommendation systems for scientific workflow design. However, most approaches miss the very sequential nature of workflows.…”

Section: Introductionmentioning

confidence: 99%

Improving workflow design by mining reusable tasks

Tosta¹,

Braganholo

Murta

et al. 2015

J Braz Comput Soc

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“…Marinho et al [2017] propose an approach named ExpLine that allows for scientists to model their experiment in multiple levels of abstraction. It is based on the concept of Experiment Lines [de Oliveira et al 2010]. Based on an abstract representation of the experiment, the scientist can derive concrete and executable workflows.…”

Section: Related Workmentioning

confidence: 99%

Managing Hypothesis of Scientific Experiments with PhenoManager

Ramos

Porto²,

Oliveira

2021

jidm

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Scientific research based on computer simulations is complex since it may involve managing the enormous volumes of data and metadata produced during the life cycle of a scientific experiment, from the formulation of hypotheses to its final evaluation. This wealth of data needs to be structured and managed in a way that makes sense to scientists so that relevant knowledge can be extracted to contribute to the scientific research process. In addition, when it comes to the scope of the scientific project as a whole, it may be associated with several different scientific experiments, which in turn may require executions of different scientific workflows, which makes the task rather arduous. All of this can become even more difficult if we consider that the project tasks must be associated with the execution of such simulations (which may take hours or even days), that the hypotheses of a phenomenon need validation and replication, and that the project team may be geographically dispersed. This article presents an approach called PhenoManager that aims at helping scientists managing their scientific projects and the cycle of the scientific method as a whole. PhenoManager can assist the scientist in structuring, validating, and reproducing hypotheses of a phenomenon through configurable computational models in the approach. For the evaluation of this article was used SciPhy, a scientific workflow in the field of bioinformatics, concluding that the proposed approach brings gains without considerable performance losses.

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“…Long-term provenance data is fundamental for enabling reproducibility and different kinds of post analysis [9][10][11], but these solutions do not allow for provenance analysis during the course of a workflow execution. Even though the workflow execution log could be browsed, this is far from provenance data query.…”

Section: Introductionmentioning

confidence: 99%

Using Domain-Specific Data to Enhance Scientific Workflow Steering Queries

Gonçalves

Oliveira

Ocaña

et al. 2012

Lecture Notes in Computer Science

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Abstract. In scientific workflows, provenance data helps scientists in understanding, evaluating and reproducing their results. Provenance data generated at runtime can also support workflow steering mechanisms. Steering facilities for workflows is considered a challenge due to its dynamic demands during execution. To steer, for example, scientists should be able to suspend (or stop) a workflow execution when the approximate solution meets (or deviates) preset criteria. These criteria are commonly evaluated based on provenance data (execution data) and domain-specific data. We claim that the final decision on whether to interfere on the workflow execution may only become feasible when workflows can be steered by scientists using provenance data enriched with domain-specific data. In this paper we propose an approach based on specialized software components, named Data Extractor (DE), to acquire domain-specific data from data files produced during a scientific workflow execution. DE gathers domain-specific data from produced data files and associates it to existing provenance data on the provenance repository. We have evaluated the proposed approach using a real bioinformatics workflow for comparative genomics executed in SciCumulus cloud workflow parallel engine.

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GExpLine: A Tool for Supporting Experiment Composition

Cited by 7 publications

References 13 publications

Improving workflow design by mining reusable tasks

Improving workflow design by mining reusable tasks

Managing Hypothesis of Scientific Experiments with PhenoManager

Using Domain-Specific Data to Enhance Scientific Workflow Steering Queries

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