Towards a Taxonomy of Provenance in Scientific Workflow Management Systems

Cruz, Sérgio Manuel Serra da; Campos, María Luiza Machado; Mattoso, Marta

doi:10.1109/services-i.2009.18

Cited by 72 publications

(46 citation statements)

References 27 publications

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“…The techniques have been developed having in mind compute-intensive applications disregarding important information sources such the size or the structure of data, to say nothing of data provenance [4] (i.e. the origin and transformations suffered by the data during the execution of an application).…”

Section: Related Workmentioning

confidence: 99%

Ensemble learning of runtime prediction models for gene-expression analysis workflows

et al. 2015

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The adequate management of scientific workflow applications strongly depends on the availability of accurate performance models of sub-tasks. Numerous approaches use machine learning to generate such models autonomously, thus alleviating the human effort associated to this process. However, these standalone models may lack robustness, leading to a decay on the quality of information provided to workflow systems on top. This paper presents a novel approach for learning ensemble prediction models of tasks runtime. The ensemble-learning method entitled bootstrap aggregating (bagging) is used to produce robust ensembles of M5P regression trees of better predictive performance than could be achieved by standalone models. Our approach has been tested on gene expression analysis workflows. The results show that the ensemble method leads to significant prediction-error reductions when compared with learned standalone models. This is the first initiative using ensemble learning for generating performance prediction models. These promising results encourage further research in this direction.

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Section: Related Workmentioning

confidence: 99%

Ensemble learning of runtime prediction models for gene-expression analysis workflows

et al. 2015

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“…In this study, we use provenance annotations about field observations, to improve the efficiency and accuracy of patterns recognition algorithms, previously developed by our research group [5,6]. The pattern recognition algorithm performs the following steps: first, it reduces the definition of the colored images about 70 % in order to speed the performance of further procedures; second, the reduced colored image is converted to black and white images; third, the reduced color images are used to generate novel images, containing only red, green and black pixels.…”

Section: Foliage Disease Pattern Recognitionmentioning

confidence: 99%

Early Discovery of Tomato Foliage Diseases Based on Data Provenance and Pattern Recognition

Nunes

Werly

Vianna

et al. 2015

Lecture Notes in Computer Science

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Abstract. This work presents an approach focused in enhancing the quality of tomato crops. We are developing and using low cost computational strategies to support early detection of the late blight. Our approach consorts tomatoes cultivars in an experimental field with inexpensive computer-aided resources based on Web and Android mobile tools in which workers collect scouting data and annotations and take images about the state of the crop, and in image filtering techniques and pattern recognition to detect foliage diseases on tomatoes images. In this study, we use provenance metadata about field observations, images and farmers' annotations as well, to improve the efficiency and accuracy of the patterns recognition algorithms. Our identification method achieved a hit rate of 94.12 %, using a reduced set of digital images of the tomato crops.

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“…Like so, surveyed techniques have been developed having in mind compute-intensive applications disregarding important information sources such the size or the structure of data, to say nothing of data provenance [4] (i.e. the origin and transformations suffered by the data during the execution of an application).…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning of Run-Time Prediction Models for Data-Intensive Scientific Workflows

Monge

Holec

Železný

et al. 2014

Communications in Computer and Information Science

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Abstract. Workflow applications for in-silico experimentation involve the processing of large amounts of data. One of the core issues for the efficient management of such applications is the prediction of tasks performance. This paper proposes a novel approach that enables the construction models for predicting task's running-times of data-intensive scientific workflows. Ensemble Machine Learning techniques are used to produce robust combined models with high predictive accuracy. Information derived from workflow systems and the characteristics and provenance of the data are exploited to guarantee the accuracy of the models.The proposed approach has been tested on Bioinformatics workflows for Gene Expressions Analysis over homogeneous and heterogeneous computing environments. Obtained results highlight the convenience of using ensemble models in comparison with single/standalone prediction models. Ensemble learning techniques permitted reductions of the prediction error up to 24.9% in comparison with single-model strategies.

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Towards a Taxonomy of Provenance in Scientific Workflow Management Systems

Cited by 72 publications

References 27 publications

Ensemble learning of runtime prediction models for gene-expression analysis workflows

Ensemble learning of runtime prediction models for gene-expression analysis workflows

Early Discovery of Tomato Foliage Diseases Based on Data Provenance and Pattern Recognition

Ensemble Learning of Run-Time Prediction Models for Data-Intensive Scientific Workflows

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