53 research outputs found
Context-Aware Notebook Search in a Jupyter-Based Virtual Research Environment
Computational notebook environments such as the Jupyter play an increasingly important role in data-centric research for prototyping computational experiments, documenting code implementations, and sharing scientific results. Effectively discovering and reusing notebooks available on the web can reduce repetitive work and facilitate scientific innovations. However, general-purpose web search engines (e.g., Google Search) do not explicitly index the contents of notebooks, and notebook repositories (e.g., Kaggle and GitHub) require users to create domain-specific queries based on the metadata in the notebook catalogs, which fail to capture the working contexts in the notebook environment. This poster presents a Context-aware Notebook Search Framework (CANSF) to enable a researcher to seamlessly discover external notebooks based on semantic contexts of the literate programming activities in the Jupyter environment.Non
SPIRIT: A Microservice-Based Framework for Interactive Cloud Infrastructure Planning
The IaaS model provides elastic infrastructure that enables
the migration of legacy applications to cloud environments. Many cloud
computing vendors such as Amazon Web Services, Microsoft Azure, and
Google Cloud Platform offer a pay-per-use policy that allows for a sustainable
reduction in costs compared to on-premise hosting, as well as
enable users to choose various geographically distributed data centers.
Using state-of-the-art planning algorithms can help application owners to
estimate the size and characteristics of the underlying cloud inveterate.
However, it’s not always clear which is the optimal solution especially
in multi-cloud environments with complex application requirements and
QoS constraints. In this paper, we propose an open framework named
SPIRIT, which allows a user to include cloud infrastructure planning
algorithms and to evaluate and compare their solutions. SPIRIT achieves
this by allowing users to interactively study infrastructure planning algorithms
by adjusting parameters via a graphical user interface, which
visualizes the results of these algorithms. In the current prototype, we
have included from the IaaS Partial Critical Path algorithm. By taking
advantage of SPIRIT’s microservice-based architecture and its generic
interfaces a user can add to the framework, new planning algorithms.
SPIRIT can transform an abstract workflow described using the CWL
to a concrete infrastructure described using the TOSCA specification.
This way the infrastructure descriptions can be ranked on various key
performance indicators
Integrating R in a distributed scientific workflow via a Jupyter-based Environment
The Research Infrastructure Lifewatch Italy has developed a Virtual Research Environment for studies on phytoplankton ecology that includes computational services based on R, a programming language widely used for data science and ecology. Here we have verified the feasibility of a Jupyter-based research environment, the NaaVRE, which has so far been tested only with Python, for running R code in a workflow on the Cloud. The successful execution demonstrated the potentialities of R in Cloud-based research environments. However, further investigation is needed, in particular, to overcome the issue of the lack of dependencies declaration in R. The possibility of performing analyses in a workflow, combined with the computational resources of remote infrastructures, will support scientists in carrying out FAIR and innovative research in a more efficient, integrated and collaborative way.</p
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