Perspectives on automated composition of workflows in the life sciences [version 1; peer review: 2 approved]

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.Stian Soiland-Reyes was supported by BioExcel-2 Centre of Excellence, funded by European Commission Horizon 2020 programme under European Commission contract H2020-INFRAEDI-02-2018 823830. Carole Goble was supported by EOSC-Life, funded by European Commission Horizon 2020 programme under grant agreement H2020-INFRAEOSC-2018-2 824087. We gratefully acknowledge the financial support from the Lorentz Center, ELIXIR, and the Leiden University Medical Center (LUMC) that made the workshop possible. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscriptPeer Reviewed"Article signat per 33 autors/es: Anna-Lena Lamprecht , Magnus Palmblad, Jon Ison, Veit Schwämmle , Mohammad Sadnan Al Manir, Ilkay Altintas, Christopher J. O. Baker, Ammar Ben Hadj Amor, Salvador Capella-Gutierrez, Paulos Charonyktakis, Michael R. Crusoe, Yolanda Gil, Carole Goble, Timothy J. Griffin , Paul Groth , Hans Ienasescu, Pratik Jagtap, Matúš Kalaš , Vedran Kasalica, Alireza Khanteymoori , Tobias Kuhn12, Hailiang Mei, Hervé Ménager, Steffen Möller, Robin A. Richardson, Vincent Robert9, Stian Soiland-Reyes, Robert Stevens, Szoke Szaniszlo, Suzan Verberne, Aswin Verhoeven, Katherine Wolstencroft "Postprint (published version

Online Self-Healing Control Loop to Prevent and Mitigate Faults in Scientific Workflows

Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. In spite of many success stories, a key challenge for running workflow in distributed systems is failure prediction, detection, and recovery. In this paper, we present a novel online self-healing framework, where failures are predicted before they happen, and are mitigated when possible. The proposed approach is to use control theory developed as part of autonomic computing, and in particular apply the proportional-integral-derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, to mitigate faults by adjusting the inputs of the mechanism. The PID controller aims at detecting the possibility of a fault far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of the Big Data era—data footprint and memory usage. We define, implement, and evaluate PID controllers to autonomously manage data and memory usage of a bioinformatics workflow that consumes/produces over 4.4TB of data, and requires over 24TB of memory to run all tasks concurrently. Experimental results indicate that workflow executions may significantly benefit from PID controllers, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when using our proposed control loop, and faults are detected and mitigated far in advance

Four simple recommendations to encourage best practices in research software [version 1; referees: awaiting peer review]

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations. Keyword

Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging

Data provenance allows scientists in different domains validating their models and algorithms to find out anomalies and unexpected behaviors. In previous works, we described on-the-fly interpretation of (Python) scripts to build workflow provenance graph automatically and then infer fine-grained provenance information based on the workflow provenance graph and the availability of data. To broaden the scope of our approach and demonstrate its viability, in this paper we extend it beyond procedural languages, to be used for purely declarative languages such as logic programming under the stable model semantics. For experiments and validation, we use the Answer Set Programming solver oClingo, which makes it possible to formulate and solve stream reasoning problems in a purely declarative fashion. We demonstrate how the benefits of the provenance inference over the explicit provenance still holds in a declarative setting, and we briefly discuss the potential impact for declarative programming, in particular for instance-driven debugging of the model in declarative problem solving

Apoio à avaliação da qualidade de dados em eScience : uma abordagem baseada em proveniência

Orientador: Claudia Maria Bauzer MedeirosTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Qualidade dos dados é um problema recorrente em todos os domínios da ciência. Os experimentos analisam e manipulam uma grande quantidade de conjuntos de dados gerando novos dados para serem (re) utilizados por outros experimentos. A base para a obtenção de bons resultados científicos está fortemente associada ao grau de qualidade de tais da- dos. No entanto, os dados utilizados nos experimentos são manipulados por uma diversa variedade de usuários, os quais visam interesses diferentes de pesquisa, utilizando seus próprios vocabulários, metodologias de trabalho, modelos, e necessidades de amostragem. Considerando este cenário, um desafio em ciência da computação é oferecer soluções que auxiliem aos cientistas na avaliação da qualidade dos seus dados. Diferentes esforços têm sido propostos abordando a avaliação de qualidade. Alguns trabalhos salientam que os atributos de proveniência dos dados poderiam ser utilizados para avaliar qualidade. No entanto, a maioria destas iniciativas aborda a avaliação de um atributo de qualidade específico, frequentemente focando em valores atômicos de dados. Isto reduz a aplicabilidade destas abordagens. Apesar destes esforços, há uma necessidade de novas soluções que os cientistas possam adotar para avaliar o quão bons seus dados são. Nesta pesquisa de doutorado, apresentamos uma abordagem para lidar com este problema, a qual explora a noção de proveniência de dados. Ao contrário de outras abordagens, nossa proposta combina os atributos de qualidade especificados dentro de um contexto pelos especialistas e os metadados que descrevem a proveniência de um conjunto de dados. As principais contribuições deste trabalho são: (i) a especificação de um framework que aproveita a proveniência dos dados para obter informação de qualidade, (ii) uma metodologia associada a este framework que descreve os procedimentos para apoiar a avaliação da qualidade, (iii) a proposta de dois modelos diferentes de proveniência que possibilitem a captura das informações de proveniência, para cenários fixos e extensíveis, e (iv) a validação dos itens (i) a (iii), com suas discussões via estudos de caso em agricultura e biodiversidadeAbstract: Data quality is a recurrent concern in all scientific domains. Experiments analyze and manipulate several kinds of datasets, and generate data to be (re)used by other experiments. The basis for obtaining good scientific results is highly associated with the degree of quality of such datasets. However, data involved with the experiments are manipulated by a wide range of users, with distinct research interests, using their own vocabularies, work methodologies, models, and sampling needs. Given this scenario, a challenge in computer science is to come up with solutions that help scientists to assess the quality of their data. Different efforts have been proposed addressing the estimation of quality. Some of these efforts outline that data provenance attributes should be used to evaluate quality. However, most of these initiatives address the evaluation of a specific quality attribute, frequently focusing on atomic data values, thereby reducing the applicability of these approaches. Taking this scenario into account, there is a need for new solutions that scientists can adopt to assess how good their data are. In this PhD research, we present an approach to attack this problem based on the notion of data provenance. Unlike other similar approaches, our proposal combines quality attributes specified within a context by specialists and metadata on the provenance of a data set. The main contributions of this work are: (i) the specification of a framework that takes advantage of data provenance to derive quality information; (ii) a methodology associated with this framework that outlines the procedures to support the assessment of quality; (iii) the proposal of two different provenance models to capture provenance information, for fixed and extensible scenarios; and (iv) validation of items (i) through (iii), with their discussion via case studies in agriculture and biodiversityDoutoradoCiência da ComputaçãoDoutora em Ciência da Computaçã

Unsupervised, Efficient and Semantic Expertise Retrieval

We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World Wide Web. 201