Virtual Reality applications on Cultural Heritage are increasing, according to a general trend towards virtual reproduction and interaction mediated by the computer system. The effects of this trend, both on education and research, are still far from being completely tested and defined. The aim of the MediaEvo Project is to develop a multi-channel and multi-sensory platform for the edutainment in Cultural Heritage, towards integration of human sciences and new data processing technologies, for the realization of a digital didactic game oriented to the knowledge of medieval history and society. The developing of the project has enhanced interactions among historical, pedagogical and ICT researches, morphological inquiries, data management systems, by means of the definition of a virtual immersive platform for playing and educating. The platform is also intended to collect feedback and validate hypothesis and findings coming from researchers. This essay introduces the questions related to the educative use of ICT and describes the steps of the reconstruction of the town of Otranto in the Middle Ages: data collection and integration, organization of work and software applications.

An efficient, secure and interoperable data platform solution has been developed in the TESSA project to provide fast navigation and access to the data stored in the data archive, as well as a standard-based metadata management support. The platform mainly targets scientific users and the situational sea awareness high-level services such as the decision support systems (DSS). These datasets are accessible through the following three main components: The Data Access Service (DAS), the Metadata Service and the Complex Data Analysis Module (CDAM). The DAS allows access to data stored in the archive by providing interfaces for different protocols and services for downloading, variables selection, data subsetting or map generation. Metadata Service is the heart of the information system of the TESSA products and completes the overall infrastructure for data and metadata management. This component enables data search and discovery and addresses interoperability by exploiting widely adopted standards for geospatial data. Finally, the CDAM represents the back-end of the TESSA DSS by performing on-demand complex data analysis tasks.

The Pocket-Finder algorithm identifies the location of ligand binding sites in a protein and is a fundamental component for a range of applications including molecular docking, de novo drug design and structural identification and comparison of functional sites. In this paper, we propose a parallel version of the Pocket-Finder algorithm. The proposed parallel algorithm uses a geometrical approach to locate favorable binding sites and has been MPI-enabled for parallel execution. The proposed algorithm has been applied on a small test of 15 proteins and 2 proteins complexes. The algorithm gets very interesting results when applied to large proteins.

In the High Performance Computing context, the performance evaluation of a parallel algorithm is carried out mainly by considering the elapsed time for running the parallel application with both different number of cores and different problem sizes (for scaled speedup). Typically, parallel applications embed mechanisms to efficiently use the allocated resources, guaranteeing for example a good load balancing and reducing the parallel overhead. Unfortunately, this assumption is not true for coupled models. These models were born from the coupling of stand-alone climate models. The component models are developed independently from each other and they follow different development roadmaps. Moreover, they are characterized by different levels of parallelization as well as different requirements in terms of workload and they have their own scalability curve. Considering a coupled model as a single parallel application, we can note the lacking of a policy useful to balance the computational load on the available resources. This work tries to address the issues related to the performance evaluation of a coupled model as well as answering the following questions: once a given number of processors has been allocated for the whole coupled model, how does the run have to be configured in order to balance the workload? How many processors must be assigned to each of the component models? The methodology here described has been applied to evaluate the scalability of the CMCC-MED coupled model designed by the ANS Division of the CMCC. The evaluation has been carried out on two different computational architectures: a scalar cluster, based on IBM Power6 processors, and a vector cluster, based on NEC-SX9 processors.

In spatial health research, it is necessary to consider not only the spatial-temporal patterns of diseases, but also external environmental factors, such as the effects of climate change on air quality, that may influence the insurgence or progression of diseases (e.g. respiratory and cardiovascular diseases, cancer, male human infertility, etc.). In this paper, we propose a Spatial Data analysis Infrastructure (SDI) for the analysis of health pathologies related to environmental factors and, more specifically, to climate change. The main goal is the development of a new methodology to predict and manage health risks, finding correlations between diseases and air pollution due to climatic factors. The presented SDI consists of different modules. A gynecological-obstetrical clinical folder application has been developed to collect and manage clinical data. Anonymous and geo-referenced patients data are extracted from the clinical folder application and data mining techniques, such as a hot spot analysis based on the Getis-Ord Gi∗ statistics, have been applied to the gathered data by exploiting the Hadoop framework. The results of the analysis are displayed in a web application that provides data visualization through geographical maps, using Geographical Information Systems (GIS) technology. This prototype, combining big data, data mining techniques, and GIS technology, represents an innovative approach to find correlations between spatial environmental factors and the insurgence of health diseases.

The aim of the MediaEvo Project is to develop a multi-sensory platform for the edutainment in Cultural Heritage towards integration of human sciences and new data processing technologies, for the realization of a digital didactic game oriented to the knowledge of medieval history and society. The developing of the project has enhanced interactions among historical, pedagogical and ICT researches, by means of the definition of a virtual immersive platform for playing and educating and has permitted to investigate some navigation and interaction modalities among players for education purposes. In this paper we present some results of the MediaEvo Project that has led the researchers to use the reconstruction of the city of Otranto in the Middle Ages in order to determine the conditions for testing more elements of interaction in a virtual environment and a multisensory mediation in which merge objects, subjects and experiential context. With the aim to make interaction easier for users without any experience of navigation in a virtual world and more efficient for trained users, we use the Wiimote and the Balance Board of Nintendo in order to increase the sense of immersion in the virtual environment.

In this paper we describe a grid problem solving environment we developed for financial applications. We based its development on a portlet framework we have specifically developed and on a set of Web APIs that encapsulate all grid control and computation logic. Even though nowadays grid portals are characterized by various and different features and are implemented in very differing programming languages and technologies, we thought that they have many structural aspects in common. For this reason we decided to design and implement a set of Grid specific Web APIs, that we called GRB WAPI. Through them, a portal developer will not have to deal with grid technical details and will be able to manage a high level design. A portal developer will be able to concentrate on some other aspects that concern presentation, such as portal usability and functionality. We discarded the idea of developing a traditional library in order to free portal developers from a particular implementation technology. Thanks to this choice the portal presentation logic can be implemented in any web technology and can be on a different server.

The visualization of 3D models of the patient’s body emerges as a priority in surgery. In this paper two different visualization and interaction systems are presented: a virtual interface and a low cost multi-touch screen. The systems are able to interpret in real-time the user’s movements and can be used in the surgical pre-operative planning for the navigation and manipulation of 3D models of the human body built from CT images. The surgeon can visualize both the standard patient information, such as the CT image dataset, and the 3D model of the patient’s organs built from these images. The developed virtual interface is the first prototype of a system designed to avoid any contact with the computer so that the surgeon will be able to visualize models of the patient’s organs and to interact with these moving the finger in the free space. The multi-touch screen provides a custom user interface developed for doctor needs that allows users to interact, for surgical pre-operative planning purposes, both with the 3D model of the patient’s body built from medical images, that with the image dataset.

Hepatic cancer is one of the most common solid cancers in the world. As surgery of hepatic cancer is seldom applicable, different solutions have been found to cure this disease. One of these is Liver Radiofrequency Ablation. This technique consists in a needle insertion inside the liver parenchyma in order to reach the tumor and in an injection of a radiofrequency current to cause tumor cell necrosis for hyperthermia. The needle placement task is really difficult because surgeon uses ultrasound, CT or MRI two-dimensional image to guide the needle. In this paper we present an Augmented Reality system to help the surgeon to place the needle as best as possible; the application can also help the surgeon during the preoperative planning because it offers various visualization modality of 3D models of the patient’s organs obtained from the medical images.

The practice of Minimally Invasive Surgery is becoming more and more widespread and adopted as an alternative to the classical procedure. This technique presents many advantages for the patients, but also some limitations for the surgeons. In particular, the lack of depth in perception and the difficulty in estimating the distance of the specific structures in laparoscopic surgery can impose limits on delicate dissection or suturing. The presence of new systems for the pre-operative planning can be of great help to the surgeon. The use of the Augmented Reality technology shows a way forward in bringing the direct advantage of the visualization of the open surgery back to minimally invasive surgery and can increase for the physician the view of the organs with information obtained from the image processing of the patient. The developed application allows the surgeon to get information about the patient and her/his pathology, visualizing and interacting with the 3D models of the organs built from the patient’s medical images, measuring the dimensions of the organs and deciding the best insertion points of the trocars in the patient’s body. This choice can be visualized on the real patient using the Augmented Reality technology.

A WorkFlow Management System (WFMS) is a fundamental componentenabling to integrate data, applications and a wide set of project resources. Although a number of scientific WFMSs support this task, many analysis pipelines require large-scale Grid computing infrastructures to cope with their high compute and storage requirements. Such scientific workflows complicate the management of resources, especially in cases where they are offered by several resource providers, managed by different Grid middleware, since resource access must be synchronised in advance to allow reliable workflow execution. Different types of Grid middleware such as gLite, Unicore and Globus are used around the world and may cause interoperability issues if applications involve two or more of them. In this paperwe describe the ProGenGrid Workflow Management System which the main goal is to provide interoperability among these different grid middleware when executing workflows. It allows the composition of batch; parameter sweep and MPI based jobs. The ProGenGrid engine implements the logic to execute such jobs by using a standard language OGF compliant such as JSDL that has been extended for this purpose. Currently, we are testing our system on some bioinformatics case studies in the International Laboratory of Bioinformatics (LIBI) Project (www.libi.it).

The present knowledge of protein structures at atomic level derives from some 60,000 molecules. Yet the exponential ever growing set of hypothetical protein sequences comprises some 10 million chains and this makes the problem of protein structure prediction one of the challenging goals of bioinformatics. In this context, the protein representation with contact maps is an intermediate step of fold recognition and constitutes the input of contact map predictors. However contact map representations require fast and reliable methods to reconstruct the specific folding of the protein backbone. Methods. In this paper, by adopting a GRID technology, our algorithm for 3D reconstruction FT-COMAR is benchmarked on a huge set of non redundant proteins (1716) taking random noise into consideration and this makes our computation the largest ever performed for the task at hand. Results: We can observe the effects of introducing random noise on 3D reconstruction and derive some considerations useful for future implementations. The dimension of the protein set allows also statistical considerations after grouping per SCOP structural classes. Conclusions: All together our data indicate that the quality of 3D reconstruction is unaffected by deleting up to an average 75% of the real contacts while only few percentage of randomly generated contacts in place of non-contacts are sufficient to hamper 3D reconstruction. © 2011 Vassura et al; licensee BioMed Central Ltd.

In this paper the use of augmented realty and cloud computing technology to enrich the scenes of cultural heritage contexts is proposed. The main objective is to develop a mobile application capable to improve user cultural experience during city sightseeing through the addition of detailed digital contents related to the site or the monument he is watching. The Wikitude SDK is the software library and framework used for the mobile application development. The huge amount of culturale dig- ital contents (mainly represented by images) justifies the exploitation of a cloud computing environment to obtain an innovative, multi-platform and user friendly augmented reality enriched solution. In particular, for the purpose of our research we’ve chosen KVM and OpenNebula open-source cloud platform as private cloud technology, since it exhibits great features like openness, flexibility, simplicity and scalability. The result of the work is validated through test beds realized both in laboratory, using images captured from PC display, and in a real environment.

A climate model represents a multitude of processes on a variety of timescales and space scales: a canonical example of multi-physics multi-scale modeling. The underlying climate system is physically characterized by sensitive dependence on initial conditions, and natural stochastic variability, so very long integrations are needed to extract signals of climate change. Algorithms generally possess weak scaling and can be I/O and/or memory-bound. Such weak-scaling, I/O, and memory-bound multi-physics codes present particular challenges to computational performance. <br><br> Traditional metrics of computational efficiency such as performance counters and scaling curves do not tell us enough about real sustained performance from climate models on different machines. They also do not provide a satisfactory basis for comparative information across models. codes present particular challenges to computational performance. <br><br> We introduce a set of metrics that can be used for the study of computational performance of climate (and Earth system) models. These measures do not require specialized software or specific hardware counters, and should be accessible to anyone. They are independent of platform and underlying parallel programming models. We show how these metrics can be used to measure actually attained performance of Earth system models on different machines, and identify the most fruitful areas of research and development for performance engineering. codes present particular challenges to computational performance. <br><br> We present results for these measures for a diverse suite of models from several modeling centers, and propose to use these measures as a basis for a CPMIP, a computational performance model intercomparison project (MIP).

We present four CUDA based parallel implementations of the Space-Saving algorithm for determining frequent items on a GPU. The first variant exploits the open-source CUB library to simplify the implementation of a user's defined reduction, whilst the second is based on our own implementation of the parallel reduction. The third and the fourth, built on the previous variants, are meant to improve the performance by taking advantage of hardware based atomic instructions. In particular, we implement a warp based ballot mechanism to accelerate the Space-Saving updates. We show that our implementation of the parallel reduction, coupled with the ballot based update mechanism, is the fastest, and provides extensive experimental results regarding its performance.

A case study on climate models intercomparison data analysis addressing several classes of multi-model experiments is being implemented in the context of the EU H2020 INDIGO-DataCloud project. Such experiments require the availability of large amount of data (multi-terabyte order) related to the output of several climate models simulations as well as the exploitation of scientific data management tools for large-scale data analytics. More specifically, the paper discusses in detail a use case on precipitation trend analysis in terms of requirements, architectural design solution, and infrastructural implementation. The experiment has been tested and validated on CMIP5 datasets, in the context of a large scale distributed testbed across EU and US involving three ESGF sites (LLNL, ORNL, and CMCC) and one central orchestrator site (PSNC).

This work describes the optimization and paralleliza- tion of the OASIS3 coupler. Performance evaluation and profiling have been carried out by means of the CMCC-MED coupled model, developed at the Euro- Mediterranean Centre for Climate Change (CMCC) and currently running on a NEC SX9 cluster. Our experiments highlighted that extrapolation (accom- plished by the extrap function) and interpolation (im- plemented from the scriprmp function) transforma- tions take the most time. Optimization concerned I/O operations reducing coupling time by 27%. Paral- lelization of OASIS3 represents a further step towards overall improvement of the whole coupled model. Our proposed parallel approach distributes fields over a pool of available processes. Each process applies cou- pling transformations to its assigned fields. This ap- proach restricts parallelization level to the number of coupling fields. However, it can be fully combined with a parallelization approach considering the geo- graphical domain distribution. Finally a quantitative comparison of the parallel coupler with the OASIS3 pseudo-parallel version is proposed.

Nowadays grid portals are characterized by various and different features and are implemented in very differing programming languages and technologies, still having many structural aspects in common. This paper describes a RESTful Web API, named GRB_WAPI, specifically developed for grid computing that encapsulates all grid control and computation logic need to build a grid portal. Through the adoption of this API a portal developer doesn’t not have to deal with grid technical details focusing just on the high level design of her system and on some other aspects that concern presentation, such as portal usability and functionality. The idea of developing a traditional library has been discarded in order to free portal developers from a particular implementation technology. Thanks to this choice the portal presentation logic can be implemented in any web technology and can be deployed on a different server. Traditional Web Services and SOAP protocol approach has been discarderd in order to adopt a RESTful approach to make the Web APIs lighter and also to take advantage of some other aspects illustrated in the paper.

This chapter introduces and puts in context Grids, Clouds, and Virtualization. Grids promised to deliver computing power on demand. However, despite a decade of active research, no viable commercial grid computing provider has emerged. On the other hand, it is widely believed—especially in the Business World—that HPC will eventually become a commodity. Just as some commercial consumers of electricity have mission requirements that necessitate they generate their own power, some consumers of computational resources will continue to need to provision their own supercomputers. Clouds are a recent business-oriented development with the potential to render this eventually as rare as organizations that generate their own electricity today, even among institutions who currently consider themselves the unassailable elite of the HPC business. Finally, Virtualization is one of the key technologies enabling many different Clouds. We begin with a brief history in order to put them in context, and recall the basic principles and concepts underlying and clearly differentiating them. A thorough overview and survey of existing technologies provides the basis to delve into details as the reader progresses through the book.

In the last years, most enterprises and IT organizations have adopted virtualization and cloud computing solutions to achieve features such as flexibility, elasticity, fault tolerance, high availability and reliability for their computational, storage and networking resource infrastructures. Moreover, recent advances in Linux containers and the emergence of technologies as Docker are revolutionizing the way of developing and deploying web and large scale distributed applications.

Robotic Surgery is a current procedure in endosurgery, but literature has not addressed the learning curve for the use of the robotic assisted surgery. The learning curve for robot surgical procedures varies widely. Apart from innate skill, learning curves are composed of at least two fundamentals related to the volume of cases and the incidence rate. Commonly cited reasons include lack of adequate training in residency programs because of the time devoted to abdominal, vaginal, and obstetric procedures, lack of available and adequate training opportunities outside of dedicated fellowships, lack of proctors and mentor surgeons in communities to help to further advance the skills of younger surgeons, and lack of desire to leave established surgical practices to try to develop skills requiring long learning curves to master. Currently, the training involves practice with the surgical robot in either pig or human fresh tissue in a laboratory environment in order to become familiar with the functions of the robot, the attachment of the robotic arms to the robotic trocars, and the overall functions of the robotic console. In this chapter authors reviewed current literature on learning curve in robotic assisted surgery and screened problems linked to robotic surgical skills.

Phytoplankton is a quality element for determining the ecological status of transitional water ecosystems. In routine analysis, bio-volume and surface area of phytoplankton are the most studied morphometric descriptors. Bio-volume can be estimated by comparing the algae with similar three-dimensional geometric forms and determining their volume, by measuring the linear dimensions required for its calculation with images acquired by an inverse microscope. Software such as LUCIA-G (Laboratory Imaging) determines, in an automatic way, only the linear dimensions of simple forms such as circle or ellipse, approximated at a given algae, whereas complex forms require the intervention of an operator by selecting the start and end points of linear dimensions with obvious introduction of human error. In this paper, we propose a novel methodology for detecting phytoplankton algae and by measuring linear dimensions of 42 geometrical forms to automatically compute their area and bio-volume, that has been implemented in a novel software, named LUISA, for image analysis.

We present FDCMSS, a new sketch-based algorithm for mining frequent items in data streams. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms. It works in the time fading model, mining data streams according to the cash register model. We formally prove its correctness and show, through extensive experimental results, that our algorithm outperforms λ-HCount, a recently developed algorithm, with regard to speed, space used, precision attained and error committed on both synthetic and real datasets.

We deal with the problem of detecting frequent items in a stream under the constraint that items are weighted, and recent items must be weighted more than older ones. This kind of problem naturally arises in a wide class of applications in which recent data is considered more useful and valuable with regard to older, stale data. The weight assigned to an item is therefore a function of its arrival timestamp. As a consequence, whilst in traditional frequent item mining applications we need to estimate frequency counts, we are instead required to estimate decayed counts. These applications are said to work in the time fading model. Two sketch-based algorithms for processing time-decayed streams have been recently published independently near the end of 2016. The FSSQ algorithm, besides a sketch, also uses an additional data structure called Quasi-Heap to maintain frequent items. FDCMSS, our algorithm, cleverly combines key ideas borrowed from forward decay, the Count-Min sketch and the Space Saving algorithm. Therefore, it makes sense to compare and contrast the two algorithms in order to fully understand their strengths and weaknesses. We show, through extensive experimental results, that FSSQ is better for detecting frequent items than for frequency estimation. The use of the Quasi-Heap data structure slows down the algorithm owing to the huge number of maintenance operations. Therefore, FSSQ may not be able to cope with high-speed data streams. FDCMSS is better suitable for frequency estimation; moreover, it is extremely fast and can be used in the context of high-speed data streams and for the detection of frequent items as well, since its recall is always greater than 99%, even when using an extremely tiny amount of space. Therefore, FDCMSS proves to be an overall good choice when considering jointly the recall, precision, average relative error and the speed.

This work introduces Ophidia, a big data analytics research effort aiming at supporting the access, analysis and mining of scientific (n-dimensional array based) data. The Ophidia platform extends, in terms of both primitives and data types, current relational database system implementations (in particular MySQL) to enable efficient data analysis tasks on scientific array-based data. To enable big data analytics it exploits well-known scientific numerical libraries, a distributed and hierarchical storage model and a parallel software framework based on the Message Passing Interface to run from single tasks to more complex dataflows. The current version of the Ophidia platform is being tested on NetCDF data produced by CMCC climate scientists in the context of the international Coupled Model Intercomparison Project Phase 5 (CMIP5).

The MediaEvo Project aims to develop a multi-channel and multi-sensory platform for edutainment in cultural heritage for the realization of a digital didactic game oriented towards the knowledge of medieval history and society by means of the integration of human sciences and new data processing technologies. During the project it has been possible to test the possible interactions between historical research, morphological inquiries, data management systems and the definition of a virtual immersive platform for playing and educating. The platform has also proved to be a means for validating hypotheses and findings formulated by researchers. This paper introduces the theoretical questions related to the educative use of Virtual Reality technology and describes the steps of the reconstruction of the town of Otranto in the Middle Ages: data collection and integration, new historical acquisitions, organization of work, peripherals and software applications.

Given an array A of n elements and a value 2≤k≤n, a frequent item or k-majority element is an element occurring in A more than n/k times. The k-majority problem requires finding all of the k-majority elements. In this paper, we deal with parallel shared-memory algorithms for frequent items; we present a shared-memory version of the Space Saving algorithm, and we study its behavior with regard to accuracy and performance on many and multi-core processors, including the Intel Phi accelerator. We also investigate a hybrid MPI/OpenMP version against a pure MPI-based version. Through extensive experimental results, we prove that the MPI/OpenMP parallel version of the algorithm significantly enhances the performance of the earlier pure MPI version of the same algorithm. Results also prove that for this algorithm the Intel Phi accelerator does not introduce any improvement with respect to the Xeon octa-core processor.

The sharing and integration of health care data such as medical history, pathology, therapy, radiology images, etc., is a key requirement for improving the patient diagnosis and in general the patient care. Today, many EPR (Electronic Patient Record) systems are present both in the same or different health centers and record a huge amount of data regarding a patient. In most cases the care treatment of a patient involves different healthcare facilities, including the cares provided by the family doctors. Managing these data, typically petabytes or terabytes in size, and optimizing the applications (image analysis, data mining, etc.) for these architectures is one of the challenges that must be tackled. Therefore, there is a clear need for the design and implementation of new scalable approaches to deal with the associated information overload and cognitive complexity issues. A possible solution involves considering a simplification of data coming from different EPRs, in a structured schema, typically called a meta-EPR. Owing to the security of patient data, each health center manages its own meta-EPR whereas a framework integrates these data among different sites. This work addresses the issue of sharing and integrating health care data, proposing a meta-EPR, based on Peer-to-peer (P2P) technology for data fusion. We describe an implementation of a distributed information service, that shares meta-EPRs and provides aggregation of relevant clinical information about patients based on a structured P2P overlay.

COSMO-CLM is a non-hydrostatic parallel atmospheric model, developed by the CLM-Community starting from the Local Model (LM) of the German Weather Service. Since 2005, it is the reference model used by the german researchers for the climate studies on different temporal scales (from few to hundreds of years) with a spatial resolution from 1 up to 50 kilometers. It is also used and developed from other meteorological research centres belonging to the Consortium for Small-scale Modelling (COSMO). The present work is focused on the analysis of the CCLM model from the computational point of view. The main goal is to verify if the model can be optimised by means of an appropriate tuning of the input parameters, to identify the performance bottlenecks and to suggest possible approaches for a further code optimisation. We started analysing if the strong scalability (which measures the improvement factor due to the parallelism given a fixed domain size) can be improved acting on some parameters such as the subdomain shape, the number of processes dedicated to the I/O operations, the output frequency and the communication strategies. Then we profiled the code to highlight the bottlenecks to the scalability and finally we performed a detailed performance analysis of the main kernels using the roofline model.

The present work aims at evaluating the scalabil- ity performance of a high-resolution global ocean biogeo- chemistry model (PELAGOS025) on massive parallel archi- tectures and the benefits in terms of the time-to-solution re- duction. PELAGOS025 is an on-line coupling between the Nucleus for the European Modelling of the Ocean (NEMO) physical ocean model and the Biogeochemical Flux Model (BFM) biogeochemical model. Both the models use a par- allel domain decomposition along the horizontal dimen- sion. The parallelisation is based on the message passing paradigm. The performance analysis has been done on two parallel architectures, an IBM BlueGene/Q at ALCF (Argonne Leadership Computing Facilities) and an IBM iDataPlex with Sandy Bridge processors at the CMCC (Euro Mediterranean Center on Climate Change). The outcome of the analysis demonstrated that the lack of scalability is due to several factors such as the I/O operations, the memory contention, the load unbalancing due to the memory structure of the BFM component and, for the BlueGene/Q, the absence of a hybrid parallelisation approach.

We deal with the problem of preference-based matchmaking of computational resources belonging to a grid. We introduce CP–Nets, a recent development in the field of Artificial Intelligence, as a means to deal with user’s preferences in the context of grid scheduling. We discuss CP–Nets from a theoretical perspective and then analyze, qualitatively and quantitatively, their impact on the matchmaking process, with the help of a grid simulator we developed for this purpose. Many different experiments have been setup and carried out, and we report here our main findings and the lessons learnt.

Within the EU IS-ENES project, the deployment of an e-infrastructure providing climate scientists with an efficient virtual proximity to distributed data and distributed computing resources is required. The access point of this infrastructure is represented by the v.E.R.C. (virtual Earth system modelling Resource Centre) web portal. It allows the Earth System Models (ESMs) scientists to run complex distributed workflows for executing ESM experiments and accessing to ESM data. The work describes the deployment of a grid prototype environment for running multi-model ensembles experiments. Considering existing grid infrastructures and services, the design of this grid prototype has been lead by the necessity to build a framework that leverage the external services offered within the European HPC ecosystem, e.g. DEISA, PRACE. The prototype allows exploiting advanced grid services, namely GRB services, developed at the University of Salento, Italy, and basic grid services offered by the Globus Toolkit middleware for submitting and monitoring the ensemble runs. The prototype has been deployed involving three sites: CMCC, DKRZ and BSC. A case study related to the HRT159, a global coupled ocean-atmosphere general circulation model (AOGCM) developed by CMCC-INGV, has been considered.

Reliable and timely information on the environmental conditions at sea is key to the safety of professional and recreational users as well as to the optimal execution of their activities. The possibility of users obtaining environmental information in due time and with adequate accuracy in the marine and coastal environment is defined as sea situational awareness (SSA). Without adequate information on the environmental meteorological and oceanographic conditions, users have a limited capacity to respond, which has led to loss of lives and to large environmental disasters with enormous consequent damage to the economy, society and ecosystems. Within the framework of the TESSA project, new SSA services for the Mediterranean Sea have been developed. In this paper we present SeaConditions, which is a web and mobile application for the provision of meteorological and oceanographic observation and forecasting products. <br><br> Model forecasts and satellite products from operational services, such as ECMWF and CMEMS, can be visualized in SeaConditions. In addition, layers of information related to bathymetry, sea level and ocean-colour data (chl <i>a</i> and water transparency) are displayed. Ocean forecasts at high spatial resolutions are included in the version of SeaConditions presented here. <br><br> SeaConditions provides a user-friendly experience with a fluid zoom capability, facilitating the appropriate display of data with different levels of detail. SeaConditions is a single point of access to interactive maps from different geophysical fields, providing high-quality information based on advanced oceanographic models. <br><br> The SeaConditions services are available through both web and mobile applications. The web application is available at <a hrefCombining double low line"www.sea-conditions.com" targetCombining double low line"-blank">www.sea-conditions.com</a> and is accessible and compatible with present-day browsers. Interoperability with GIS software is implemented. User feedback has been collected and taken into account in order to improve the service. The SeaConditions iOS and Android apps have been downloaded by more than 105000 users to date (May 2016), and more than 100000 users have visited the web version.

Grid portals are web gateways aiming at concealing the underlying infrastructure through a pervasive, transparent, user-friendly, ubiquitous and seamless access to heterogeneous and geographically spread resources (i.e. storage, computational facilities, services, sensors, network and databases). The Climate- G Portal is the web gateway of the Climate-G testbed (an interdisciplinary research effort involving scientists both in Europe and US) and it is devoted to climate change research studies. The main goal of this paper is to present the Climate-G Portal providing a complete understanding of the international context, discussing its main requirements, challenges, architecture and key functionalities, and finally carrying out and presenting a multi-dimensional analysis of the Climate-G Portal, starting from a general schema proposed and discussed in this work.

Grid computing is a well-known technology to share resources such as high-performance computers, sensors, observation devices, data and databases across dynamic and multi-institutional Virtual Organizations (VOs). The complexity of data management in a grid environment comes from the distribution, scale, growing rate, heterogeneity, dynamicity of data sources. The data grid layer (grid data management system) in the overall grid computing software stack must provide a complete support in terms of access, integration, replication, monitoring, mining, management of data sources in a grid environment. The main goal of this work is to present the data access layer of the GRelC system architecture discussing in detail the vision, the main challenges, the internal architecture, the security framework and a real test case in the Earth Science and Environmental domains. © 2010 Elsevier Inc. All rights reserved.

Over the last 20 years, the open-source community has provided more and more software on which the world's high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics processing units. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.

The NEMO (Nucleus for European Modeling of the Ocean) oceanic model is one of the most widely used by the climate community. It is exploited with different configurations in more than 50 research projects for both long and short-term simulations. Computational requirements of the model and its implementation limit the exploitation of the emerging computational infrastructure at peta and exascale. A deep revision and analysis of the model and its implementation were needed. The paper describes the performance evaluation of the last release of the model, based on MPI parallelization, on the MareNostrum platform at the Barcelona Supercomputing Centre. The analysis of the scalability has been carried out taking into account different factors, i.e. the I/O system available on the platform, the domain decomposition of the model and the level of the parallelism. The analysis highlighted different bottlenecks due to the communication overhead. The code has been optimized reducing the communication weight within some frequently called functions and the parallelization has been improved introducing a second level of parallelism based on the OpenMP shared memory paradigm.

Prevention is one of the most important stages in wildfire and other natural hazard management. Fire Danger Rating Systems (FDRSs) have been adopted by many countries to enhance wildfire prevention and suppression planning. With the aim to provide real-Time fire danger forecasts and finer-scale fire behaviour analysis, an operational fire danger prevention platform has been developed within the OFIDIA project (Operational FIre Danger preventIon plAtform). The OFIDIA Fire Danger Rating System platform consists of (1) a data archive for managing weather forecasting and wireless sensors data, (2) a data analytics platform for post-processing weather data and for computing fire danger indices, and (3) a web application system for the visualization of weather and fire index maps and related timeseries. The OFIDIA platform is also connected to a Wireless Sensor Network (WSN) that gathers data from several sites in the Apulia (Italy) and Epirus (Greece) regions. The WSN is made by a primary station and several wireless sensors dislocated in wooded areas, the data acquisition process relates to variables like air temperature, relative humidity, wind speed and direction, precipitation, solar radiation, and fuel moisture.

The successive over relaxation (SOR) is a variant of the iterative Gauss-Seidel method for solving a linear system of equations Ax = b. The SOR algorithm is used within the Nucleus for European Modelling of the Ocean (NEMO) model for solving the elliptical equation for the barotropic stream function. The NEMO performance analysis shows that the SOR algorithm introduces a significant communication overhead. Its parallel implementation is based on the red-black method and foresees a communication step at each iteration. An enhanced parallel version of the algorithm has been developed by acting on the size of the overlap region to reduce the frequency of communications. The overlap size must be carefully tuned for reducing the communication overhead without increasing the computing time. This work describes an analytical performance model of the SOR algorithm that can be used for establishing the optimal size of the overlap region.

The present work describes the analysis and optimisation of the PELAGOS025 configuration based on the coupling of the NEMO physic component of the ocean dynamics and the BFM (Biogeochemical Flux Model), a sophisticated biogeochemical model that can simulate both pelagic and benthic processes. The methodology here followed is characterised by the performance analysis of the original parallel code, in terms of strong scalability, the definition of the bottlenecks limiting the scalability when the number of processes increases, the analysis of the features of the most computational intensive kernels through the Roofline model which provides an insightful visual performance model for multicore architectures and which allows to measure and compare the performance of one or more computational kernels run on different hardware architectures.

Laparoscopy is the standard of treatment for many gynecological diseases, it is a very common procedure in gynaecology and it is widely accepted as the method of first choice for many gynaecological problems. A meta-analysis of 27 randomized controlled trials comparing laparoscopy and laparotomy for benign gynaecological procedures concluded that the risk of minor complications after gynaecological surgery is 40% lower with laparoscopy than with laparotomy, although the risk of major complications is similar. Laparoscopy has been considered a real alternative to laparotomy with numerous advantages: short hospital stay, less need of analgesia, low intraoperative blood loss and faster recovery time. Many researchers are in pursuit of new technologies and new tools of minimally invasive technologies for reducing laparoscopic complications. The industry responded to these demands with many innovations, such as new optical instruments and digital images, virtual and augmented reality, robotic assisted surgery, etc. In this chapter, authors discussed the possible utilization of novel technologies to reduce the risk of laparoscopic gynecological complications.

In this paper we present the approach proposed by EU H2020 INDIGO-DataCloud project to orchestrate dynamic workflows over a cloud environment. The main focus of the project is on the development of open source Platform as a Service solutions targeted at scientific communities, deployable on multiple hardware platforms, and provisioned over hybrid e-Infrastructures. The project is addressing many challenging gaps in current cloud solutions, responding to specific requirements coming from scientific communities including Life Sciences, Physical Sciences and Astronomy, Social Sciences and Humanities, and Environmental Sciences. We are presenting the ongoing work on implementing the whole software chain on the Infrastructure as a Service, PaaS and Software as a Service layers, focusing on the scenarios involving scientific workflows and big data analytics frameworks. INDIGO module for Kepler worflow system has been introduced along with the INDIGO underlying services exploited by the workflow components. A climate change data analytics experiment use case regarding the precipitation trend analysis on CMIP5 data is described, that makes use of Kepler and big data analytics services.

The main goal of the Human-Computer Interaction technology is to improve the interactions between users and computers by making computers more usable and receptive to the user's needs. The end point in the interface design would then lead to a paradigm in which the interaction with computers becomes similar to the one between human beings. This paper focuses on an application of navigation and interaction in a virtual environment using the Nintendo Wiimote and the Balance Board. The idea is to have a system of navigation control in a virtual environment based on a locomotion interface in order to make the interaction easier for users without any experience of navigation in a virtual world and more efficient for trained users. The application has been developed for the navigation and interaction in the virtual town. We chose Otranto as an example town; Otranto is located in the easternmost tip of the Italian peninsula and, due to its geographical position, the town was like a bridge between East and West. The virtual environment is a loyal representation of the town of Otranto in the Middle Ages.