Using Provenance to Improve Workflow Design

Oliveira, Frederico Tosta de; Murta, Leonardo; Werner, Cláudia Maria Lima; Mattoso, Marta

doi:10.1007/978-3-540-89965-5_15

Cited by 16 publications

(11 citation statements)

References 9 publications

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“…Our previous approach [15] was conceived to allow incremental recommendation, where, for each selected task, a single following task is suggested. Initially, the relations among tasks are extracted.…”

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%

“…We started by observing that some sequences of tasks are common to several different workflows within a given domain [11,[14][15][16]21]. For example, in a set of scientific experiments from a given domain, task D appears after a specific non-contiguous sequence of tasks A → .…”

Section: Introductionmentioning

confidence: 99%

“…We introduced a preliminary solution to this problem in a previous work [15]. In that initial solution, a single task is suggested at each step.…”

Section: Introductionmentioning

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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…We introduced a preliminary solution to this problem in a previous work [15]. In that initial solution, a single task is suggested at each step.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving workflow design by mining reusable tasks

Tosta¹,

Braganholo

Murta

et al. 2015

J Braz Comput Soc

Self Cite

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show abstract

“…The work of [8,9,10,11,12,13,14] and [15,16] consider the order of activities, input, output and data provenance. Their limitations are the need of provenance data, since not all Scientific Workflow Management System (SWMS) stores this information.…”

Section: Related Workmentioning

confidence: 99%

Combining Artificial Intelligence, Ontology, and Frequency-based Approaches to Recommend Activities in Scientific Workflows

Khouri

Digiampietri

2018

RITA

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Abstract:The number of activities provided by scientific workflow management systems is large, which requires scientists to know many of them to take advantage of the reusability of these systems. To minimize this problem, the literature presents some techniques to recommend activities during the scientific workflow construction. In this paper we specified and developed a hybrid activity recommendation system considering information on frequency, input and outputs of activities and ontological annotations. Additionally, this paper presents a modeling of activities recommendation as a classification problem, tested using 5 classifiers; 5 regressors; and a composite approach which uses a Support Vector Machine (SVM) classifier, combining the results of other classifiers and regressors to recommend; and Rotation Forest, an ensemble of classifiers. The proposed technique was compared to related techniques and to classifiers and regressors, using 10-fold-cross-validation, achieving a Mean Reciprocal Rank (MRR) at least 70% greater than those obtained by classical techniques. Keywords: recommendation system -scientific workflows -artificial intelligence -ontology Resumo: O número de atividades disponibilizados por sistemas gerenciadores de workflows científicoé muito grande e isto faz com que os cientistas precisem conhecer muitas atividades para tirar proveito da reusabilidade provida por esses sistemas. Para minimizar este problema, a literatura apresenta algumas técnicas para recomendar atividades durante a construção de um workflow científico. Neste artigo nós especificamos e desenvolvemos uma solução híbrida de sistema de recomendação de atividades utilizando informações de frequência, entrada e saída das atividades e anotação ontológica. Adicionalmente, apresentamos a modelagem da recomendação de atividades na forma de um problema de classificação em inteligência artificial, o qual foi testado com 5 classificadores; 5 regressores; uma solução composta usando Máquinas de Vetores de Suporte (SVM), combinando os resultados dos outros classificadores e regressores; e o classificador Rotation Forest. A técnica proposta foi comparada com técnicas correlatas bem como com o uso dos classificadores e regressores, usando validação cruzada com dez subconjuntos e atingiu o valor de ranqueamento recíproco médio (MRR) ao menos 70 % superior do que aqueles obtidos por técnicas clássicas Palavras-Chave: sistema de recomendação -workflows científicos -inteligência artificial -ontologias

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Service Package Recommendation for Mashup Development Based on a Multi-level Relational Network

Cao

Zhu

2016

Service-Oriented Computing

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Using Provenance to Improve Workflow Design

Cited by 16 publications

References 9 publications

Improving workflow design by mining reusable tasks

Improving workflow design by mining reusable tasks

Combining Artificial Intelligence, Ontology, and Frequency-based Approaches to Recommend Activities in Scientific Workflows

Service Package Recommendation for Mashup Development Based on a Multi-level Relational Network

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