4,136 research outputs found
Polyflow: a Polystore-compliant mechanism to provide interoperability to heterogeneous provenance graphs
Many scientific experiments are modeled as workflows. Workflows usually output massive amounts of data. To guarantee the reproducibility of workflows, they are usually orchestrated by Workflow Management Systems (WfMS), that capture provenance data. Provenance represents the lineage of a data fragment throughout its transformations by activities in a workflow. Provenance traces are usually represented as graphs. These
graphs allows scientists to analyze and evaluate results produced by a workflow. However, each WfMS has a proprietary format for provenance and do it in different granularity levels. Therefore, in more complex scenarios in which the scientist needs to interpret provenance graphs generated by multiple WfMSs and workflows, a challenge arises. To first understand the research landscape, we conduct a Systematic Literature Mapping,
assessing existing solutions under several different lenses. With a clearer understanding of the state of the art, we propose a tool called Polyflow, which is based on the concept of Polystore systems, integrating several databases of heterogeneous origin by adopting a global ProvONE schema. Polyflow allows scientists to query multiple provenance graphs in an integrated way. Polyflow was evaluated by experts using provenance data collected from real experiments that generate phylogenetic trees through workflows. The experiment results suggest that Polyflow is a viable solution for interoperating heterogeneous provenance data generated by different WfMSs, from both a usability and performance standpoint.Muitos experimentos científicos são modelados como workflows (fluxos de trabalho). Workflows produzem comumente um grande volume de dados. De forma a garantir a reprodutibilidade desses workflows, estes geralmente são orquestrados por Sistemas de Gerência de Workflows (SGWfs), garantindo que dados de proveniência sejam capturados. Dados de proveniência representam o histórico de derivação de um dado ao longo da execução do workflow. Assim, o histórico de derivação dos dados pode ser representado
por meio de um grafo de proveniência. Este grafo possibilita aos cientistas analisarem e avaliarem resultados produzidos por um workflow. Todavia, cada SGWf tem seu formato proprietário de representação para dados de proveniência, e os armazenam em diferentes granularidades. Consequentemente, em cenários mais complexos em que um cientista precisa analisar de forma integrada grafos de proveniência gerados por múltiplos workflows, isso se torna desafiador. Primeiramente, para entender o campo de pesquisa, realizamos um Mapeamento Sistemático da Literatura, avaliando soluções existentes sob diferentes lentes. Com uma compreensão mais clara do atual estado da arte, propomos uma ferramenta chamada Polyflow, inspirada em conceitos de sistemas Polystore, possibilitando a integração de várias bases de dados heterogêneas por meio de uma interface de consulta única que utiliza o ProvONE como schema global. Polyflow permite que cientistas
submetam consultas em múltiplos grafos de proveniência de maneira integrada. Polyflow foi avaliado em conjunto com especialistas usando dados de proveniência coletados de workflows reais que apoiam o estudo de geração de árvores filogenéticas. O resultado da avaliação mostrou a viabilidade do Polyflow para interoperar semanticamente dados de proveniência gerado por distintos SGWfs, tanto do ponto de vista de desempenho quanto de usabilidade
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
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