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2021 Report Open Access OPEN

ARIADNEPlus - VREs operation mid-term activity report
Assante M., Cirillo R., Dell'Amico A., Pagano P., Candela L., Frosini L., Lelii L., Mangiacrapa F., Panichi G., Sinibaldi F.
This deliverable D13.2 - "VREs Operation Mid-term Activity Report" describes the activities carried out during the first 24 months of the ARIADNEplus project within Work Package 13. Specifically, in Task 13.1 Infrastructure Operation (JRA2.1) and Task 13.3 VREs Operation (JRA2.3). It reports the procedures governing the operation of the VREs as well as the status of the aggregated resources at mid-term in the ARIADNEplus infrastructure.Source: Project report, ARIADNEplus, D13.2, 2021
Project(s): ARIADNEplus via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2020 Contribution to book Open Access OPEN

Data Processing and Analytics for Data-Centric Sciences
Candela L., Coro G., Lelii L., Panichi G., Pagano P.
The development of data processing and analytics tools is heavily driven by applications, which results in a great variety of software solutions, which often address specific needs. It is difficult to imagine a single solution that is universally suitable for all (or even most) application scenarios and contexts. This chapter describes the data analytics framework that has been designed and developed in the ENVRIplus project to be (a) suitable for serving the needs of researchers in several domains including environmental sciences, (b) open and extensible both with respect to the algorithms and methods it enables and the computing platforms it relies on to execute those algorithms and methods, and (c) open-science-friendly, i.e. it is capable of incorporating every algorithm and method integrated into the data processing framework as well as any computation resulting from the exploitation of integrated algorithms into a "research object" catering for citation, reproducibility, repeatability and provenance.Source: Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. A Reference Model Guided Approach for Common Challenges., edited by Zhao Z.; Hellström M., pp. 176–191, 2020
DOI: 10.1007/978-3-030-52829-4_10
Project(s): ENVRI PLUS via OpenAIRE

See at: link.springer.com Open Access | link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | academic.microsoft.com Restricted | link.springer.com Restricted | link.springer.com Restricted | link.springer.com Restricted


2020 Journal article Open Access OPEN

NLPHub: an e-Infrastructure-based text mining hub
Coro G., Panichi G., Pagano P., Perrone E.
Text mining involves a set of processes that analyze text to extract high-quality information. Among its large number of applications, there are experiments that tackle big data challenges using complex system architectures. However, text mining approaches are neither easy to discover and use nor easily combinable by end-users. Furthermore, they should be contextualized within new approaches to science (eg, Open Science) that ensure longevity and reuse of methods and results. This article presents NLPHub, a distributed system that orchestrates and combines several state-of-the-art text mining services that recognize spatiotemporal events, keywords, and a large set of named entities. NLPHub adopts an Open Science approach, which fosters the reproducibility, repeatability, and reusability of methods and results, by using an e-Infrastructure supporting data-intensive Science.NLPHubaddsOpenScience-compliance to the connected services through the use of representational standards for services and computations. It also manages heterogeneous service access policies and enables collaboration and sharing facilities. This article reports a performance assessment based on an annotated corpus of named entities, which demonstrates that NLPHub can improve the performance of the single-integrated processes by cleverly combining their output.Source: Concurrency and computation (Online) (2020): e5986. doi:10.1002/cpe.5986
DOI: 10.1002/cpe.5986
Project(s): PARTHENOS via OpenAIRE

See at: ISTI Repository Open Access | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | CNR ExploRA Restricted


2020 Journal article Open Access OPEN

Realizing virtual research environments for the agri-food community: the AGINFRA PLUS experience
Assante M., Boizet A., Candela L., Castelli D., Cirillo R., Coro G., Fernández E., Filter M., Frosini L., Georgiev T., Kakaletris G., Katsivelis P., Knapen R., Lelii L., Lokers R. M., Mangiacrapa F., Manouselis N., Pagano P., Panichi G., Penev L., Sinibaldi F.
The enhancements in IT solutions and the open science movement are injecting changes in the practices dealing with data collection, collation, processing, analytics, and publishing in all the domains, including agri-food. However, in implementing these changes one of the major issues faced by the agri-food researchers is the fragmentation of the "assets" to be exploited when performing research tasks, for example, data of interest are heterogeneous and scattered across several repositories, the tools modelers rely on are diverse and often make use of limited computing capacity, the publishing practices are various and rarely aim at making available the "whole story" including datasets, processes, and results. This paper presents the AGINFRA PLUS endeavor to overcome these limitations by providing researchers in three designated communities with Virtual Research Environments facilitating the use of the "assets" of interest and promote collaboration.Source: Concurrency and computation (Online) 33 (2020). doi:10.1002/cpe.6087
DOI: 10.1002/cpe.6087
Project(s): AGINFRA PLUS via OpenAIRE

See at: ISTI Repository Open Access | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | CNR ExploRA Restricted | Concurrency and Computation Practice and Experience Restricted


2020 Report Open Access OPEN

Blue Cloud - D4.2: Blue Cloud VRE Common Facilities (Release 1)
Assante M., Candela L., Pagano P., Dell'Amico A., Coro G., Cirillo R., Frosini L., Lelii L., Lettere M., Mangiacrapa F., Panichi G., Sinibaldi F.
The Blue-Cloud project plans to pilot a cyber platform bringing together and providing access to multidisciplinary data from observations and models, analytical tools, and computing facilities essential to support research to understand better and manage the many aspects of ocean sustainability. To achieve this goal, Blue-Cloud is developing, deploying, and operating the Blue-Cloud platform whose architecture consists of two families of components: (a) the Blue Cloud Data Discovery and Access service component to serve federated discovery and access to 'blue data' infrastructures; and (b) the Blue Cloud Virtual Research Environment (VRE) component to provide a Blue Cloud VRE as a federation of computing platforms and analytical services. This deliverable presents the Blue Cloud Virtual Research Environment constituents by focusing on both new services and revised existing services that have been developed in the reporting period to serve the needs of the Blue Cloud community. In particular, this deliverable describes a total of 11 services and components. These services and components contribute functionalities to the Blue Cloud VRE Enabling Framework (Identity and Access Management, VRE Management), Collaborative framework (Workspace and Social Networking), Analytics Framework (Software and Algorithm Importer, Smart Executor), Publishing Framework (Catalogue Service) and improved support for RStudio, JupyterHub, ShinyProxy, and Docker Applications. The services are described below by reporting their design principles, architectures, and main features. The deliverable also describes the procedures and approaches governing services and components released by highlighting how Gitea (as Git hosting service), Jenkins (as automation server), and Maven (as project management and comprehension tool) are used to guarantee continuous integration processes. Services and components discussed in this deliverable contribute to 11 gCube open-source software system releases (from gCube 4.16 up to gCube 4.25.1) and are in the pipeline for the next ones. They have been used to develop and operate the Virtual Laboratories of the Blue Cloud gateway https://blue-cloud.d4science.org and its underlying infrastructure. At the time of this deliverable (November 2020), the gateway hosts a total of 8 VREs and VLabs, including five specifically conceived to support the co-development of some of the Blue-Cloud demonstrators (namely, the Aquaculture Atlas Generation for Demonstrator #5, the Blue-Cloud Lab for several demonstrators, the GRSF pre for Demonstrator #4, the Marine Environmental Indicators for Demonstrator #3, the Zoo-Phytoplankton EOV for Demonstrator #1). This gateway and its tools serve more than 400 users that (since January 2020) performed a total of more than 5000 working sessions, more than 1700 accesses to the Workspace, and more than 750 analytics tasks. These exploitation and uptake indicators are likely to grow in the coming months thanks to data updates and continued use, further development of existing VLabs, and finally, the creation of new ones.Source: Project Report, Blue Cloud, D4.2, 2020
Project(s): Blue Cloud via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2019 Journal article Open Access OPEN

Reconstructing 3D virtual environments within a collaborative e-infrastructure
Coro G., Palma M., Ellenbroek A., Panichi G., Nair T., Pagano P.
Sets of two dimensional images are insufficient to capture the development in time and space of three-dimensional structures. The 2D 'flattening' of photographs results in a significant loss of features especially if the photos were taken by one person. Automatically collecting and aligning photos in order to render 3D structures from 2D images without specialized equipment, is currently a complex process that requires specialist knowledge with often limited results. In this paper, an Open Science oriented workflow is proposed where an on-line file system is used to share photos of an object or an environment and to produce a virtual reality scene as a navigable 3D reconstruction that can be shared with other people. Our workflow is based on a distributed e-Infrastructure and overcomes common limitations of other approaches by having all the used technology integrated on the same platform and by not requiring specialist knowledge. A performance evaluation of the 3D reconstruction process embedded in the workflow is reported against a commercial software and an open-source software in terms of computational efficiency and reconstruction accuracy, and three marine science use cases are reported to show potential applications of the workflow.Source: Concurrency and Computation: Practice and Experience 31 (2019). doi:10.1002/cpe.5028
DOI: 10.1002/cpe.5028
Project(s): MERCES via OpenAIRE

See at: ISTI Repository Open Access | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | CNR ExploRA Restricted


2019 Journal article Open Access OPEN

The gCube system: delivering virtual research environments as-a-service
Assante M., Candela L., Castelli D., Cirillo R., Coro G., Frosini L., Lelii L., Mangiacrapa F., Marioli V., Pagano P., Panichi G., Perciante C., Sinibaldi F.
Important changes have characterised research and knowledge production in recent decades. These changes are associated with developments in information technologies and infrastructures. The processes characterising research and knowledge production are changing through the digitalization of science, the virtualisation of research communities and networks, the offering of underlying systems and services by infrastructures. This paper gives an overview of gCube, a software system promoting elastic and seamless access to research assets (data, services, computing) across the boundaries of institutions, disciplines and providers to favour collaboration-oriented research tasks. gCube's technology is primarily conceived to enable Hybrid Data Infrastructures facilitating the dynamic definition and operation of Virtual Research Environments. To this end, it offers a comprehensive set of data management commodities on various types of data and a rich array of "mediators" to interface well-established Infrastructures and Information Systems from various domains. Its effectiveness has been proved by operating the D4Science.org infrastructure and serving concrete, multidisciplinary, challenging, and large scale scenarios.Source: Future generation computer systems 95 (2019): 445–453. doi:10.1016/j.future.2018.10.035
DOI: 10.1016/j.future.2018.10.035
Project(s): AGINFRA PLUS via OpenAIRE, BlueBRIDGE via OpenAIRE, ENVRI PLUS via OpenAIRE, EOSCpilot via OpenAIRE

See at: ISTI Repository Open Access | Future Generation Computer Systems Open Access | Future Generation Computer Systems Restricted | Future Generation Computer Systems Restricted | Future Generation Computer Systems Restricted | Future Generation Computer Systems Restricted | CNR ExploRA Restricted | Future Generation Computer Systems Restricted | www.sciencedirect.com Restricted


2019 Journal article Open Access OPEN

Enacting open science by D4Science
Assante M., Candela L., Castelli D., Cirillo R., Coro G., Frosini L., Lelii L., Mangiacrapa F., Pagano P., Panichi G., Sinibaldi F.
The open science movement is promising to revolutionise the way science is conducted with the goal to make it more fair, solid and democratic. This revolution is destined to remain just a wish if it is not supported by changes in culture and practices as well as in enabling technologies. This paper describes the D4Science offerings to enact open science-friendly Virtual Research Environments. In particular, the paper describes how complete solutions suitable for realising open science practices can be achieved by integrating a social networking collaborative environment with a shared workspace, an open data analytics platform, and a catalogue enabling to effectively find, access and reuse every research artefact.Source: Future generation computer systems (2019): 555–563. doi:10.1016/j.future.2019.05.063
DOI: 10.1016/j.future.2019.05.063
Project(s): AGINFRA PLUS via OpenAIRE, BlueBRIDGE via OpenAIRE, ENVRI PLUS via OpenAIRE, EOSCpilot via OpenAIRE

See at: ISTI Repository Open Access | Future Generation Computer Systems Restricted | Future Generation Computer Systems Restricted | Future Generation Computer Systems Restricted | Future Generation Computer Systems Restricted | Future Generation Computer Systems Restricted | CNR ExploRA Restricted | Future Generation Computer Systems Restricted


2019 Conference article Open Access OPEN

An Open Science System for Text Mining
Coro G., Panichi G., Pagano P.
Text mining (TM) techniques can extract high-quality information from big data through complex system architectures. However, these techniques are usually difficult to discover, install, and combine. Further, modern approaches to Science (e.g. Open Science) introduce new requirements to guarantee reproducibility, repeatability, and re-usability of methods and results as well as their longevity and sustainability. In this paper, we present a distributed system (NLPHub) that publishes and combines several state-of-the art text mining services for named entities, events, and keywords recognition. NLPHub makes the integrated methods compliant with Open Science requirements and manages heterogeneous access policies to the methods. In the paper, we assess the benefits and the performance of NLPHub on the I-CAB corpus.Source: CLiC-it 2019 Italian Conference on Computational Linguistic, pp. 1–7, Bari, Italy, 13-15/11/2019
Project(s): PARTHENOS via OpenAIRE

See at: disi.unitn.it Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2019 Report Restricted

PARTHENOS - Deliverable 6.6 - PARTHENOS Cloud Infrastructure
Pagano P., Assante M., Frosini L., Manghi P., Bardi A., Sinibaldi F., Cirillo R., Panichi G.
"D6.6 PARTHENOS cloud infrastructure" is the revised and final version of "D6.1 PARTHENOS cloud infrastructure". This deliverable reports the PARTHENOS e-infrastructure architecture: the hardware and the services. Hardware is organized as a dynamic cloud of virtual machines, supporting computation and storage, while the services are organized into e-infrastructure middleware, storage, and end user services.Source: Project report, PARTHENOS, Deliverable D6.6, pp.1–63, 2019
Project(s): PARTHENOS via OpenAIRE

See at: data.d4science.net Restricted | CNR ExploRA Restricted


2018 Report Open Access OPEN

ENVRIplus - Interoperable data processing services for environmental RI projects: prototype
Candela L., Cirillo R., Coro G., Pagano P., Panichi G.
This deliverable documents the implementation of the data processing solution defined in D7.1 "Interoperable data processing for environmental RIs projects: system design". The actual deliverable consists of the software realising the envisaged solution and the instances of it made available to the ENVRI community in the large. The distinguishing features of the proposed solution are (a) to be suitable for serving the needs of scientists involved in ENVRI RIs, (b) to be open and extensible both with respect to the algorithms and methods it enables and the computing platforms it relies on to execute those algorithms and methods, (c) to be open-science-friendly, i.e. it is capable of incorporating every algorithm and method integrated into the data processing framework as well as any computation resulting from the exploitation of integrated algorithms into a "research object" catering for citation, reproducibility, repeatability and provenance. The proposed solution is part of a larger software system named gCube and has been provisioned to ENVRI RIs via several Virtual Research Environments operated by D4Science.Source: Project report, ENVRIplus, Deliverable D7.2, 2018
Project(s): ENVRI PLUS via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2017 Journal article Open Access OPEN

Cloud computing in a distributed e-infrastructure using the Web processing service standard
Coro G., Panichi G., Scarponi P., Pagano P.
New Science paradigms have recently evolved to promote open publication of scientific findings as well as multi-disciplinary collaborative approaches to scientific experimentation. These approaches can face modern scientific challenges but must deal with large quantities of data produced by industrial and scientific experiments. These data, so-called "Big Data", require to introduce new Computer Science systems to help scientists cooperate, extract information, and possibly produce new knowledge out of the data. E-Infrastructures are distributed computer systems that foster collaboration between users and can embed distributed and parallel processing systems to manage Big Data. However, in order to meet modern Science requirements, e-Infrastructures impose several requirements to computational systems in turn, e.g. being economically sustainable, managing community-provided processes, using standard representations for processes and data, managing Big Data size and heterogeneous representations, supporting reproducible Science, collaborative experimentation, and cooperative online environments, managing security and privacy for data and services. In this paper, we present a Cloud computing system (gCube DataMiner) that meets these requirements and operates in an e-Infrastructure, while sharing characteristics with state-of-the-art Cloud computing systems. To this aim, DataMiner uses the Web Processing Service standard of the Open Geospatial Consortium and introduces features like collaborative experimental spaces, automatic installation of processes and services on top of a flexible and sustainable Cloud computing architecture. We compare DataMiner with another mature Cloud computing system and highlight the benefits our system brings, the new paradigms requirements it satisfies, and the applications that can be developed based on this system.Source: Concurrency and computation (Online) 29 (2017). doi:10.1002/cpe.4219
DOI: 10.1002/cpe.4219
Project(s): BlueBRIDGE via OpenAIRE

See at: ISTI Repository Open Access | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | Concurrency and Computation Practice and Experience Restricted | onlinelibrary.wiley.com Restricted | Concurrency and Computation Practice and Experience Restricted | CNR ExploRA Restricted


2017 Report Open Access OPEN

ENVRI PLUS - Interoperable data processing for environmental ri projects: system design.
Candela L., Coro G., Pagano P., Panichi G, Atkinson M., Filgueira R., Bailo D., Enell C. F., Fiebig M., Haslinger F., Hellström M, Vermeulen A., Lankreijer H., Huber R., Joussaume S., Guglielmo F., Mendez V.
Data processing is a very wide area or domain because of a series of characteristics including the contexts resulting from diverse application scenarios, the great variety of processes to be enabled, the large set of enabling technologies and solutions. One of the consequences of this large variety is that each software solution for data processing only manages to address parts, i.e. it is difficult to imagine a single solution that is equally suitable for any (or even most) application scenarios and contexts. This deliverable illustrates that scope and diversity by reporting detailed practices and requirements from seven of the ENVRI RIs. It then describes the design of a single data processing solution that will help meet a substantial range of requirements in a representative range of contexts. That approach is conceived to be (a) suitable for serving the needs of scientists involved in ENVRI RIs, (b) open and extensible both with respect to the algorithms and methods it enables and the computing platforms it relies on to execute those algorithms and methods, (c) open-science-friendly, i.e. it is capable of incorporating every algorithm and method integrated into the data processing framework as well as any computation resulting from the exploitation of integrated algorithms into a "research object" catering for citation, reproducibility, repeatability and provenance.Source: Project report, ENVRI PLUS, Deliverable D7.1, pp.1–55, 2017
Project(s): ENVRI PLUS via OpenAIRE

See at: CNR ExploRA Open Access


2017 Report Open Access OPEN

AGINFRA PLUS - Open Science Data Analytics Technologies D3.1
Candela L., Cirillo R., Coro G., Lelii L., Pagano P., Panichi G., Scarponi P., Sinibaldi F.
Deliverable D3.1 "Open Science Data Analytics Technologies" is a deliverable of type Demonstrator meaning that it manifests in artefacts (software releases) other than reports. In particular, the deliverable is about the software realising the Data Analytics & Processing Layer of the AGINFRA+. This software is part of a large software system named gCube (www.gcube-system.org). The gCube system offers a large array of services supporting the entire lifecycle underlying a research activity (data management and collation, analytics, collaboration, sharing) and the possibility to combine these services in Virtual Research Environments1. In the context of AGINFRA PLUS the following gCube components have been primarily exploited, consolidated and enhanced to serve the analytics needs arising in the context of the project use cases. DataMiner, i.e. a service enacting its users to perform data analytics tasks by relying on an array of analytics methods and a distributed and heterogeneous computing infrastructure. This service is available by a web-based GUI as well as via a web-based API based on the OGC WPS standard. SAI (Statistical Algorithm Importer), i.e. a service enacting its users to make available their own analytics methods via the DataMiner service. In addition to that, the entire analytics solution made available for AGINFRA PLUS cases counts on (i) a shared workspace realising a cloud-based file manager for managing content of interest and sharing this content with co-workers, (ii) a social networking area enabling users to post messages and have discussions, (iii) a flexible catalogue enabling to publish and discover items of interest including "research objects" resulting from an analytics task. This technology is deployed in its latest version in every Virtual Research Environment supporting AGINFRA PLUS cases2. The major enhancements to the technology pertaining to AGINFRA PLUS have been included in three gCube major releases3 4.7 (October 2017), 4.8 (November 2017), and 4.9 (under production).In particular, with these releases a new"black-box" oriented approach (https://wiki.gcubesystem. org/gcube/Statistical_Algorithms_Importer:_Java_Project#Black_Box_Integration)has been envisaged and implemented to enact analytics method owners and developers to easily integrate theirsolutions into the DataMinerservice. Among the supported black-box typologies there is that for KNIME workflows, i.e. analytics methods implemented by a KNIME workflow. KNIME is among the key technologies supporting the Food Safety Risk Assessment cases. In order to enact the execution of KNIME-based black-boxes, the distributed computing part of the data analytics platform has been extended to integrate the KNIME execution engine. Other cases are counting on the same mechanism to integrate entire applications (WOFOST4) as well as Python-based methods.Source: Project report, AGINFRA PLUS, Deliverable D3.1, pp.1–4, 2017
Project(s): AGINFRA PLUS via OpenAIRE

See at: CNR ExploRA Open Access | support.d4science.org Open Access


2016 Report Open Access OPEN

How-to implement algorithms for DataMiner
Coro G., Panichi G.
In this document we describe how to implement an algorithm that should run on the D4Science DataMiner service. DataMiner is a cross-usage service that provides users and services with tools for performing data mining operations. Specifically, it offers a unique access to perform data mining and statistical operations on heterogeneous data, which may reside either at client side, in the form of comma-separated values files, or be remotely hosted, possibly in a database. The DataMiner service is able to take inputs and execute the operation requested by a client or a user, by invoking the most suited computational facility from a set of available computational resources. Executions can run either on multi-core machines or on different computational platforms, such as D4Science and other different private and commercial Cloud providers.Source: ISTI Technical reports, 2016
Project(s): BlueBRIDGE via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2016 Report Open Access OPEN

The DataMiner manager web interface
Panichi G., Coro G.
In this document we describe the DataMiner Manager Web interface that allows interacting with the gCube DataMiner service. DataMiner is a cross-usage service that provides users and services with tools for performing data mining operations. Specifically, it offers a unique access to perform data mining and statistical operations on heterogeneous data, which may reside either at client side, in the form of comma-separated values files, or be remotely hosted, possibly in a database. The DataMiner service is able to take inputs and execute the operation requested by a client or a user, by invoking the most suited computational facility from a set of available computational resources. Executions can run either on multi-core machines or on different computational platforms, such as D4Science and other different private and commercial Cloud providers.Source: ISTI Technical reports, 2016
Project(s): BlueBRIDGE via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2016 Report Open Access OPEN

The statistical algorithms importer
Panichi G., Coro G.
In this document we describe the Statistical Algorithms Importer Web interface. The Statistical Algorithms Importer (SAI) is a tool to import algorithms in the D4Science e-Infrastructure. Currently, it supports R scripts integration. SAI separates R scripts development from its deployment in the infrastructure in a very flexible way. After the first deployment, made in collaboration with the e-Infrastructure team, script developers can modify and update their scripts by themselves, without the intervention of the e-Infrastructure team.Source: ISTI Technical reports, 2016

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2016 Journal article Open Access OPEN

A Web application to publish R scripts as-a-Service on a Cloud computing platform
Coro G., Panichi G., Pagano P.
Prototype scripting is the base of most models in computational biology and environmental sciences. Scientists making prototype scripts (e.g. using R and Matlab) often need to share results and make their models used also by other scientists on new data. To this aim, one way is to publish scripts as-a-Service, possibly under a recognized standard (e.g. the Web Processing Service of the Open Geospatial Consortium). Unfortunately, prototype scripts are not generally meant to be transformed into Web services, which require managing multi-tenancy, concurrency etc. Often, porting prototype scripts to more efficient programming languages is not affordable, because this operation demands for time, competencies and money. For this reason, Web services are becoming smart enough to integrate prototype scripts directly and possibly make them run efficiently (e.g. WPS4R ). In this paper, we present an interface (Statistical Algorithms Importer, SAI) that allows scientists to easily and quickly import R scripts onto a distributed e-Infrastructure, which publishes the scripts as-a-Service and manages multi-tenancy and concurrency. Additionally, it allows scientists to update their scripts without following long software re-deploying procedures each time. SAI relies on the D4Science e-Infrastructure , a distributed computer system supporting large-scale resource sharing and Cloud computing, via the definition of Virtual Research Environments (VREs).Source: Bollettino di geofisica teorica ed applicata (Testo stamp.) 52 (2016): 51–53.
Project(s): BlueBRIDGE via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2016 Software Unknown

gCube Accounting manager portlet - A solution to retrieve and display infrastructure's accounting information [Release 1.0 , 01 July 2016]
Panichi G.
A portlet representing a gCube-based solution to retrieve and display infrastructure's accounting information. It provides data-visulization tools for timewise-aggregated and key-filtered information, and it also allows for data export in different formats, i.e. json, csv, pdf, jpg and svg. gCube is a software system enabling the realization and operation of hybrid data infrastructures capable of supporting the concept of Virtual Research Environments (VREs).Project(s): BlueBRIDGE via OpenAIRE

See at: CNR ExploRA | www.gcube-system.org


2015 Software Unknown

PandA Service - Web service rest per la condivisione di dati del personale CNR
Diciotti R., Panichi G.
PandA Service - Web Service Rest for sharing data on staff employed at CNR

See at: panda.isti.cnr.it | CNR ExploRA