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The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe. © 2020 The Author(s).

Despite the recent dramatic advances in the computational and data processing capacities of the commodity solutions, a numerous scientific, socioeconomic and industrial “grand challenges” exists that could be solved only through capabilities that exceed the current solutions by orders of magnitude. To demonstrate the feasibility of addressing these problems necessitating processing of exascale data sets, novel architectural approaches are needed. These architectures need to support efficient service composition and balancing infrastructure- and user-centric points of view of exascale infrastructures and services. This combination of bottom-up and top-down approaches aims at narrowing the gap between infrastructure services and paving the way towards future high capacity generations einfrastructure. The resulting architecture will help us provide computing solutions to exascale challenges within the H2020 project PROCESS11PROCESS project homepage https://www.process-project.eu/.

Big Data is a challenge in many dimensions one of which is its processing. The often complicated and lengthy processing requires specialized programming paradigms to distribute and scale the computing power. Such paradigms often focus on ordering tasks to fit an underlying architecture. In this paper, we propose a new and complementary way of reasoning about data processing by describing complex data processing as set of state transitions, specifically, a non-deterministic finite automata which captures the essence of complex data processing. Through a P2P implementation of this model we demonstrate the dynamism, intrinsic scalability and adeptness to Cloud architectures.

We present MUSE, a software framework for tying together existing computational tools for different astrophysical domains into a single multiphysics, multiscale workload. MUSE facilitates the coupling of existing codes written in different languages by providing inter-language tools and by specifying an interface between each module and the framework that represents a balance between generality and computational efficiency. This approach allows scientists to use combinations of codes to solve highly-coupled problems without the need to write new codes for other domains or significantly alter their existing codes. MUSE currently incorporates the domains of stellar dynamics, stellar evolution and stellar hydrodynamics for a generalized stellar systems workload. MUSE has now reached a "Noah’s Ark" milestone, with two available numerical solvers for each domain. MUSE can treat small stellar associations, galaxies and everything in between, including planetary systems, dense stellar clusters and galactic nuclei. Here we demonstrate an examples calculated with MUSE: the merger of two galaxies. In addition we demonstrate the working of MUSE on a distributed computer. The current MUSE code base is publicly available as open source at http://muse.li.

We present performance measurements of direct gravitational N-body simulation on the grid, with and without specialized (GRAPE-6) hardware. Our intercontinental virtual organization consists of three sites, one in Tokyo, one in Philadelphia and one in Amsterdam. We run simulations with up to 196608 particles for a variety of topologies. In many cases, high performance simulations over the entire planet are dominated by network bandwidth rather than latency. Using a global grid of GRAPEs our calculation time remains dominated by communication over the entire range of N, which was limited due to the use of three sites. Increasing the number of particles will result in a more efficient execution and for N >= 2 · 10 to the power of 6 we expect the computation time to overtake the communication time. We compare our results with a performance model and find that our results are in accordance with the predicted values.

Understanding of processes in which calcium signaling is involved is of fundamental importance in systems biology and has many applications in medicine. In this paper we have studied the particular case of the complex calcium-calcineurin-MCIP-NFAT signaling network in cardiac myocytes, the understanding of which is critical for treatment of pathologic hypertrophy and heart failure. By including some most recent experimental findings, we constructed a computational model totally based on biochemical principles. The model can correctly predict the mutant (MCIP1-/-) behavior under different stress such as PO (pressure overload) and CaN* (activated calcineurin) overexpression.

Applying suitable application models is essential to achieve efficient execution of the applications, effective development process and assure application portability and reusability. We observe that execution environments and the cloud environment in particular, lack of tools that would make it more available from a programmer perspective. Complexity and variety of libraries and authentication methods do not correspond with simplistic functionality of those services. We approach this issue at several fields - workflow systems, domain specific languages and cloud computing. Our work focus on two points, optimizing existing application models by supplementing them with additional tools and providing new features, and develop new models that are based on our research on applications in the cloud environment.

Cloud service providers support many different standards in authentication and resource management. Libraries and APIs provided to operate in a cloud environment are not consistent and require deep understanding of internal technical aspects. Thus, we propose a framework that provides high-level language to develop applications and, thanks to its layered structure, API to modify low-level operation.

Task farming is often used to enable parameter sweep for exploration of large sets of initial conditions for large scale complex simulations. Such applications occur very often in life sciences. Available solutions enable to perform parameter sweep by creating multiple job submissions with different parameters. This paper presents an approach to farm workflows, employing service oriented paradigms using the WS-VLAM workflow manager, which provides ways to create, control, and monitor workflow applications and their components. We present two service-oriented approaches for workflow farming: task level, whereby task harness acts as services by being invoked on which task to load, and data level, where the actual task is invoked as a service with different chunks of data to process. An experimental evaluation of the presented solution is performed with a biomedical application for which 3000 simulations were required to perform a Monte Carlo study.

Nowadays, data-intensive scientific research needs storage capabilities that enable efficient data sharing. This is of great importance for many scientific domains such as the Virtual Physiological Human. In this paper, we introduce a solution that federates a variety of systems ranging from file servers to more sophisticated ones used in clouds or grids. Our solution follows a client-centric approach that loosely couples a variety of data resources that may use different technologies such as Openstack-Swift, iRODS, GridFTP, and may be geographically distributed. It is implemented as a lightweight service which does not require installation of a software on the resources it uses. In this way we are able to efficiently use heterogeneous storage resources, reduce the usage complexity of multiple storage resources, and avoid vendor lock-in in case of cloud storage. To demonstrate the usability of our approach we performed a number of experiments that assess the performance and functionality of the developed system.

lectronic notebooks are a common mechanism for scientists to document and investigate their work. With the advent of tools such as IPython Notebooks and Knitr, these notebooks allow code and data to be mixed together and published online. However, these approaches assume that all work is done in the same notebook environment. In this work, we look at generating notebook documentation from multi-environment workflows by using provenance represented in the W3C PROV model.Specifically, using PROV generated from the Ducktape workflow system, we are able to generate IPython notebooks that include results tables, provenance visualizations as well as references to the software and datasets used. The notebooks are interactive and editable, so that the user can explore and analyze the results of the experiment without re-running the workflow.We identify specific extensions to PROV necessary for facilitating documentation generation. To evaluate, we recreate the documentation website for a paper which won the Open Science Award at the ECML/PKDD 2013 machine learning conference. We show that the documentation produced automatically by our system provides more detail and greater experimental insight than the original hand-crafted documentation. Our approach bridges the gap between user friendly notebook documentation and provenance generated by distributed heterogeneous components.

Ontology development involves people with different background knowledge and expertise. It is an elaborate process, where sophisticated tools for experienced knowledge engineers are available. However, domain experts need simple tools that they can use to focus on ontology instantiation. In this paper, we propose a methodology with a separation of concern between domain experts and knowledge engineers. This separation allows domain experts to focus on information processing and ontology instantiation while providing immediate feedback to the knowledge engineers on usability of the ontology being developed. We have designed and implemented OINTED, an adaptive online ontology instance editor that supports this methodology. We present usage examples of OINTED that highlight three main features: the intuitive visualization of concepts, instances, and relationships within an ontology; the seamless integration in pre-existing problem solving environment; and the assistance in ontology evolution. OINTED complements existing tools suited for knowledge engineers by enabling immediate feedback and a shorter ontology development life cycle.

The term e-Science describes computational and data-intensive science. It has become a complementary experiment paradigm alongside the traditional in vivo and in vitro experiment paradigms. e-Science opens new doors for scientists and with it, it exposes a number of challenges such as how to organize huge datasets and coordinate distributed execution. For these challenges, a plethora of technologies and innovations have come together to enable e-Science (Foster and Kesselman 2006). Nowadays, complex scientific experiments designed following the e-Science paradigm are preformed using geographically distributed instruments, data and computing resources. The newly designed scientific experiments are costly, time-consuming, and multidisciplinary. Complex scientific experiments not only require access to geographically distributed hardware and software resources, but also extensive support to foster best practices, dissemination, and re-use.

Reproducibility of Science is considered as one of the main principles of the scientific method, and refers to the ability of an experiment to be accurately reproduced, by third person, in complex experiment every detail matters to ensure the correct reproducibility. In the context of the ICCS 2011, Elsevier organized the executable paper grand challenge a contest to improve the way scientific information is communicated and used. While during this contest the focus was on developing methods and technique to realize the idea of executable papers, in this paper we focus on the operational issues related to the creation a viable service with a predefined QoS.

Currently, most workflow management systems (WfMS) in Grid environments provide push-oriented task distribution strategies, where tasks are directly bound to suitable resources. In those scenarios the dedicated resources execute the submitted tasks according to the request of a WfMS or sometimes by support of a Meta-Scheduling service. This approach has specific problems, especially because of various conditions and constrains that have to be taken into account like local policies or the sites’ autonomy. To deal with such issues, this paper takes a closer look to the task distribution strategies. The established Grid WfMSs essentially support control-flow and data perspectives. However, they neglect the resource perspective. This paper exposes the advantages to deal with this perspective and demonstrates its feasability by a prototype implementation that integrates the missing resource patterns into UNICORE.

In this paper we propose a novel method for auto-scaling data-centric workflow tasks. Scaling is achieved through a prediction mechanism where the input data load on each task within a workflow is used to compute the estimated task execution time. Through load prediction, the framework can take informed decisions on scaling multiple workflow tasks independently to improve overall throughput and reduce workflow bottlenecks. This method was implemented in the WS-VLAM workflow system and with an image analyses workflow we show that this technique achieves faster data processing rates and reduces overall workflow makespan.

Provenance describes the process which led to the creation of a piece of data. Tracking provenance of experiment results is essential in modern environments which support conducting of in silico experiments. We present a provenance tracking approach developed as part of the virtual laboratory of the ViroLab project. The applied provenance solution is motivated by the Semantic Grid vision as an infrastructure for e-Science. Provenance data is represented in XML and modeled as ontologies described in the OWL knowledge representation language. The provenance tracking system, PROToS, has been designed and implemented to address important stages of the knowledge management lifecycle.

The Second Workshop on Teaching Computational Science, within the International Conference on Computational Science, provides a platform for discussing innovations in teaching computational sciences at all levels and contexts of higher education. This editorial provides an introduction to the work presented during the sessions.

GridSpace Engine is the central operational unit of the ViroLab Virtual Laboratory. This specific runtime environment enables access to computational and data resources by coordinating execution of experiments written in the Ruby programming language extended with virtual laboratory capabilities. Experiments harness published and semantically described services which constitute a GridSpace. The GridSpace Engine is a reliable service acting as an entry point to the Virtual Laboratory, its execution capabilities and a facade for specialized services such as Data Access Service. Moreover, owing to the provided dedicated libraries, the GridSpace Engine supports interactive execution and run-time monitoring of experiments. Furthermore, the GridSpace Engine is capable of retrieving experiment source not only from file systems but also from multiple Application Repositories accessed by dedicated adapters. Currently, our repository is based on the Subversion source code management and version control system. The GridSpace Engine is also responsible for storing obtained experimental results in the Laboratory Data Base.

We apply the finite volume method to a spherically symmetric conservation law of advection-diffusion-reaction type. For the numerical flux we use the so-called complete flux scheme. In this scheme the flux is computed from a local boundary value problem for the complete equation, including the source term. As a result, the numerical flux is the superposition of a homogeneous flux and an inhomogeneous flux. The resulting scheme is second order accurate, uniformly in the Peclet numbers.

On-line monitoring of Grid workflows is challenging since workflows are loosely coupled and highly dynamic. An efficient mechanism of automatic resource discovery is needed in order to discover new producers of workflow monitoring data fast. However, currently used Grid information systems are not suitable for this due to insufficient performance characteristics. We propose to associate the monitoring infrastructure with a P2P DHT infrastructure in order to achieve the automatic resource discovery wherein consumers of monitoring data can be notified fast about new producers. In our solution, the consumer of monitoring data can subscribe to any monitoring endpoint and the automatic resource discovery is handled transparently. We evaluate performance of the presented solution, and demonstrated a case study scenario of monitoring of a traffic management workflow.