1,309 research outputs found
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
Distributed storage and queryng techniques for a semantic web of scientific workflow provenance
In scientific workflow environments, scientists depend on provenance, which records the history of an experiment. Resource Description Framework is frequently used to represent provenance based on vocabularies such as the Open Provenance Model. For complex scientific workflows that generate large amounts of RDF triples, single-machine provenance management becomes inadequate over time. In this thesis, we research how HBase capabilities can be leveraged for distributed storage and querying of provenance data represented in RDF. We architect the ProvBase system that incorporates an HBase/Hadoop backend, propose a storage schema to hold provenance triples, and design querying algorithms to evaluate SPARQL queries in the system. We conduct an experimental study to show the feasibility of our approach
e-Social Science and Evidence-Based Policy Assessment : Challenges and Solutions
Peer reviewedPreprin
myTea: Connecting the Web to Digital Science on the Desktop
Bioinformaticians regularly access the hundreds of databases and tools that are available to them on the Web. None of these tools communicate with each other, causing the scientist to copy results manually from a Web site into a spreadsheet or word processor. myGrids' Taverna has made it possible to create templates (workflows) that automatically run searches using these databases and tools, cutting down what previously took days of work into hours, and enabling the automated capture of experimental details. What is still missing in the capture process, however, is the details of work done on that material once it moves from the Web to the desktop: if a scientist runs a process on some data, there is nothing to record why that action was taken; it is likewise not easy to publish a record of this process back to the community on the Web. In this paper, we present a novel interaction framework, built on Semantic Web technologies, and grounded in usability design practice, in particular the Making Tea method. Through this work, we introduce a new model of practice designed specifically to (1) support the scientists' interactions with data from the Web to the desktop, (2) provide automatic annotation of process to capture what has previously been lost and (3) associate provenance services automatically with that data in order to enable meaningful interrogation of the process and controlled sharing of the results
Distributed Semantic Web Data Management in HBase and MySQL Cluster
Various computing and data resources on the Web are being enhanced with
machine-interpretable semantic descriptions to facilitate better search,
discovery and integration. This interconnected metadata constitutes the
Semantic Web, whose volume can potentially grow the scale of the Web. Efficient
management of Semantic Web data, expressed using the W3C's Resource Description
Framework (RDF), is crucial for supporting new data-intensive,
semantics-enabled applications. In this work, we study and compare two
approaches to distributed RDF data management based on emerging cloud computing
technologies and traditional relational database clustering technologies. In
particular, we design distributed RDF data storage and querying schemes for
HBase and MySQL Cluster and conduct an empirical comparison of these approaches
on a cluster of commodity machines using datasets and queries from the Third
Provenance Challenge and Lehigh University Benchmark. Our study reveals
interesting patterns in query evaluation, shows that our algorithms are
promising, and suggests that cloud computing has a great potential for scalable
Semantic Web data management.Comment: In Proc. of the 4th IEEE International Conference on Cloud Computing
(CLOUD'11
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
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