Fostering the development of SPARQL interfaces to heterogeneous databases is a key to efficiently expose legacy data as RDF on the Web. To deal with the variety of modern database formats and query languages, this paper describes a two-step approach to translate a SPARQL query into an equivalent target database query. First, given an xR2RML mapping describing how native database entities can be mapped to RDF, a SPARQL query is translated into a pivot abstract query language independent of the database. In a second step, the pivot query is translated into the target database query language, considering the specific database capabilities. The paper focuses on the first step of the query translation, from SPARQL to a pivot query that takes into account join constraints and SPARQL filters, and embeds conditions entailed by matching SPARQL graph patterns with relevant mappings. It discusses the query optimisations that can be implemented at this level, and briefly describes an application to the case of MongoDB, a NoSQL document store.

International audience

The implementation of a grid network to support large-scale epidemiology analysis (based on distributed medical data sources) and medical data sharing require medical data integration and semantic alignment. In this paper, we present the GINSENG (Global Initiative for Sentinel e-Health Network on Grid) network that federates existing Electronic Health Records through a rich metamodel (FedEHR), a semantic data model (SemEHR) and distributed query toolkits. A query interface based on the VIP platform, and available through the e-ginseng.org web portal helps medical end-users in the design of epidemiological studies and the retrieval of relevant medical data sets.

This paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workflow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011.

In spite of the growing interest for grids and cloud infrastructures among scientific communities and the availability of such facilities at large-scale, achieving high performance in production environments remains challenging due to at least four factors: the low reliability of very large-scale distributed computing infrastructures, the performance overhead induced by shared facilities, the difficulty to obtain fair balance of all user jobs in such an heterogeneous environment, and the complexity of large-scale distributed applications deployment. All together, these difficulties make infrastructure exploitation complex, and often limited to experts. This paper introduces a pragmatic solution to tackle these four issues based on a service-oriented methodology, the reuse of existing middleware services, and the joint exploitation of local and distributed computing resources. Emphasis is put on the integrated environment ease of use. Results on an actual neuroscience application show the impact of the environment setup in terms of reliability and performance. Recommendations and best practices are derived from this experiment.

Querying and linking distributed and heterogeneous databases is increasingly needed, as plentiful data resources are published over the Web. This work describes the design of a versatile query system named KGRAM that supports (i) multiple query languages among which the SPARQL 1.1 standard, (ii) federation of multiple heterogeneous and distributed data sources, and (iii) adaptability to various data manipulation use cases. KGRAM provides abstractions for both the query language and the data model, thus delivering unifying reasoning mechanisms. It is implemented as a modular software suite to ease architecting and deploying dedicated data manipulation platforms. Its design integrates optimization concerns to deliver high query performance. Both KGRAM's software versatility and performance are evaluated.

Cloud computing infrastructures are providing resources on demand for tackling the needs of large-scale distributed applications. Determining the amount of resources to allocate for a given computation is a difficult problem though. This paper introduces and compares four automated resource allocation strategies relying on the expertise that can be captured in workflow-based applications. The evaluation of these strategies was carried out on the Aladdin/Grid'5000 testbed using a real application from the area of medical image analysis. Experimental results show that optimized allocation can help finding a trade-off between amount of resources consumed and applications make span.

Through the recent emergence of joint resource and network virtualization, dynamic composition and provisioning of time-limited and isolated virtual infrastructures is now possible. One other benefit of infrastructure virtualization is the capability of transparent reliability provisioning (reliability becomes a service provided by the infrastructure). In this context, we discuss the motivations and gains of introducing customizable reliability of virtual infrastructures when executing large-scale distributed applications, and present a framework to specify, allocate and deploy virtualized infrastructure with reliability capabilities. An approach to efficiently specify and control the reliability at runtime is proposed. We illustrate these ideas by analyzing the introduction of reliability at the virtual-infrastructure level on a real application. Experimental results, obtained with an actual medical-imaging application running in virtual infrastructures provisioned in the experimental large-scale Grid'5000 platform, show the benefits of the virtualization of reliability.

Production grids exhibit high failure rates hampering the development of many large scale scientific applications. End users require robust experiment production environments ensuring efficient resubmission of failed tasks. Proper parameterization of resubmission strategies is a complex problem that depends on the non-stationary workload conditions experienced by the infrastructure. In order to determine optimal resubmission parameters, probabilistic models of the overhead experienced by grid jobs are defined, taking into account the distribution of faults as measured on the infrastructure. Two strategies that can be implemented on the client side are proposed. Their models are evaluated under variable workload conditions to assess their validity along time. Their results are compared and a trade-off between usability and model accuracy is discussed.

Although Service-Oriented principles have been widely adopted by High Throughput Computing infrastructure designers, the integration between SOA and HTC is made difficult by legacy. jGASW is a framework for wrapping legacy scientific applications as Web Services and integrating them into an intensive computing-aware SOA framework. It maps complex I/O data structures to command lines and enables dynamic allocation of computing resources; including execution on local hosts or on grid infrastructures; data transfer management and support of non-functional concerns.

In this paper we propose to achieve a semantic equivalence between a visual- and a script-based workflow development paradigm. We accomplish this by building a script language which execution semantics matches an existing sophisticated, data-parallel scientific workflow language and its underlying GUI-based core workflow enactor. It facilitates multiple representations of a scientific workflow while preserving the execution semantics and enactment engine. This development caters to the need of users with different levels of expertise within a single workflow platform. A two-ways representation translator makes it possible to convert any source workflow into its semantically equivalent counter-part, and therefore use a single enactor independently of the user's preferred representation.

Resource-intensive and complex medical imaging applications can benefit from the use of scientific workflow technology for their design, rapid implementation and reuse, but at the same time they require a grid computing infrastructure to execute efficiently. In this paper we describe a technical architecture that bridges the gap between the Taverna workflow management system and the EGEE grid infrastructure. This is achieved through a novel Taverna gLite activity plugin that makes the interface between the multi-threaded, centralized workflow enactor and the massively distributed, batch-oriented grid infrastructure. The plugin significantly increases the performance of Medical Imaging workflows over an equivalent plain Taverna workflow.

With the expansion and the convergence of computing and communication, the dynamic provisioning of customized processing and networking infrastructures as well as resource virtualization are appealing concepts and technologies. Therefore, new models and tools are needed to allow users to create, trust and exploit such on-demand virtual infrastructures within wide area distributed environments. This paper proposes to combine network and system virtualization with cryptographic identification and SPKI/HIP principles to help the user communities to build and share their own resource reservoirs. These ideas are implemented in the HIPerNet framework enabling the creation and the management of customized confined execution environments in a large scale context. Based on the example of biomedical applications, the paper focuses on the security model of the HIPerNet system and develops the key aspects of our distributed security approach. Then the paper discusses and illustrates how HIPerNet solutions fulfill the security requirements of applications through different scenarios.

Production-grid users experience many system faults as well as high and variable latencies due to the scale, complexity and sharing of such infrastructures. To improve performance, they adopt different submission strategies, that are potentially aggressive for the infrastructure.

Web Services Oriented Architecture (WSOA) supports development of high quality applications based on a control--flow between services. The introduction of a loop to deal with a dataset inside an orchestration is a typical evolution use case inside a WSOA, but there is no tool support to perform such refactoring operation. In this paper we propose a new method to refactor an orchestration dealing with a variable v so that it iterates over a dataset v* automatically. This algorithm is then validated on a running software used as a validation platform by the French research project called FAROS.

In this paper, the expressiveness of the simple Scufl data-flow language is studied by showing how it can be used to implement Turing machines. To do that, several non trivial Scufl patterns such as self-looping or sub-workflows are required and we precisely explicit them. The main result of this work is to show how a complex workflow can be implemented using a very simple data-flow language. Beyond that, it shows that Scufl is a Turing complete language, given some restrictions that we discuss.

Production grids are complex and highly variable systems whose behavior is not well understood and difficult to anticipate. The goal of this study is to estimate the impact of the variability of those infrastructures on the performance of workflow-based applications. A probabilistic model of workflows execution time is proposed and evaluated. Results show that the variability of the EGEE grid infrastructure impacts the execution time of a particular medical image analysis application by a factor 2. The model gives interesting insights on the grid behavior for different application parallelization modes.

In this paper, we study grid jobs submission latency. The latency highly impacts performances on production grids, due to its high values and variations. It is particularly prejudicial for determining the status and expected duration of jobs and it makes outliers detection difficult. In previous work, a probabilistic model of the latency has been presented. It allows to estimate the best timeout value considering a given distribution of jobs latencies. This time-out value is then used in a job resubmission strategy. The purpose of this paper is to evaluate to what extent updating this model with relevant contextual parameters can help to refine the latency estimation. Experiments on the EGEE production grid show that the choice of the resource broker or the computing site has a statistically significant influence on the jobs latency. We exploit this contextual information to propose a reliable job submission strategy.

The impact of lossy compression has often been discussed in the medical area. In this study, an evaluation of the impact of lossy compression on the performance of rigid registration algorithms for medical images is proposed. The robustness, repeatability and accuracy of these algorithms is estimated through a statistical procedure for each compression ratio. Results are obtained thanks to a grid technology handling the computation cost of the method. Experiments reveal that the impact of compression is quite negligible below a significant compression ratio if the registration algorithm has a good multi-scale handling. Beyond this threshold, feature-based methods are highly penalized.

In this paper, we examine how the execution context of grid jobs can help to refine submission strategies on a production grid. On this kind of infrastructure, the latency highly impacts performances. We present experiments that quantify the dependencies between the grid latency and both internal and external context parameters on the EGEE grid infrastructure. We show how job submission managers, job execution sites and the submission date can be statistically correlated to grid performances.

Grids technologies enable the deployment of complex data-intensive scientific applications. Nonspecific scientific codes may benefit from grid computing capabilities by (i) assembling codes in workflows (code parallelism) and (ii) processing large amounts of data (data parallelism). We designed MOTEUR, a service-based workflow manager, to describe data-intensive scientific applications in a compact framework and to efficiently process the resulting computations by transparently exploiting different parallelism levels. Theoretical performances are analyzed and results are shown based on a real application to medical image databases processing

Production grids have a potential for parallel execution of a very large number of tasks but also introduce a high overhead that significantly impacts the execution of short tasks. In this work, we present a strategy to optimize the partitioning of jobs on a grid infrastructure. This method takes into account the variability and the difficulty to model a multi-user large-scale environment used for production. It is based on probabilistic estimations of the grid overhead. We first study analytically modeled environments and then we show results on a real grid infrastructure. We demonstrate that this method leads to a significant time speed-up and to a substantial saving of the number of submitted tasks with respect to a blind maximal partitioning strategy.

Data intensive medical image processing applications can easily benefit from grid capabilities. However, the setting up of complex medical experiments is not straight forward on current grid infrastructures. To ease such experiments we are developing a generic and grid-enabled workflow framework, relying on current standards. We show results on a concrete application to medical image registration assessment. We discuss the limitations induced by current standards and tools and how they were overcome for deploying the application.

Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical applications.

Grids rely on a complex interconnection of IP domains that may exhibit changing performance characteristics and may offer different quality of service (QoS) facilities. We examine the case of a biomedical application distributed over a grid and show how it may suffer from uncontrolled communication performance. Then we present the QoSINUS service that dynamically allocates the network resources to Grid flows in order to match their specific QoS requirements under different load conditions. The aim of this approach is to optimize the end to end performances the heterogeneous mix of grid flows gets from the network to enhance the individual application's performance as the overall grid infrastructure performance and utilization level. The QoSINUS service is based on the programmable network approach that offers flexibility, evolutivity and enables dynamic adaptation to network load variations. Finally results of QoSINUS experiments conducted in the context of the eToile french grid testbed based on the high speed and DiffServ capable research network infrastructure, VTHD, are presented.

In this paper, we present the implementation of a Magnetic Resonance Imaging (MRI) simulator on a GRID computing architecture. The simulation process is based on the resolution of Bloch equation [1] in a 3D space. The computation kernel of the simulator is distributed to the grid nodes using MPICH-G2 [2]. The results presented show that simulation of 3D MRI data is achieved with a reasonable cost which gives new perspectives to MRI simulations usage.

Medical data represent tremendous amount of data for which automatic analysis is increasingly needed. Grids are very promising to face today's challenging health issues such as epidemiological studies through large image data sets. However, the sensitive nature of medical data makes it difficult to widely distribute medical applications over computational grids. In this paper, we review fundamental medical data manipulation requirements and we propose a distributed data management architecture that addresses the medical data security and high performance constraints. A prototype is currently being developed inside our laboratories to demonstrate the architecture capability to face realistic distributed medical data manipulation situations.

The availability of digital imagers inside hospitals and their ever growing inspection capabilities have established digital medical images as a key component of many pathologies diagnosis, follow-up and treatment. To face the growing image analysis requirements, automated medical image processing algorithms have been developed over the two past decades. In parallel, medical image databases have been set up in health centers. Some attempts have been made to cross data coming from different origins for studies involving large databases. Grid technologies appear to be a promising tool to face the raising challenges of computational medicine. They offer wide area access to distributed databases in a secured environment and they bring the computational power needed to complete some large scale statistical studies involving image processing. In this paper, we review grid-related requirements of medical application that we illustrate through two real examples.

Medical image segmentation is often a difficult task due to the low contrast, the low signal/noise ratio and the presence of outliers in images. However, it remains a critical issue for image interpretation, pattern recognition and automatic diagnosis. Deformable models are well-suited for capturing the geometry and the shape variability of anatomical structures from medical images. Indeed, they introduce an a priori knowledge in the segmentation process that increases its robustness to noise and outliers. In this paper, we address many problems related to volumetric medical image segmentation based on deformable models including model initialization, model topology, deformation behavior and image features extraction.

In computer vision, two complementary approaches have been widely used to perform object reconstruction and registration. The deformable model framework locally applies internal and external forces to fit 3D data. The non-rigid registration framework iteratively computes the best global transformation that minimizes the distance between a template and the data. In this paper first we show that applying a global transformation on a surface model, is equivalent to applying an external force on a deformable model without any regularizing force. Second we propose a hybrid framework that combines the registration framework and the deformable models framework. Our hybrid deformation approach allows us to control the scale at which the model is deformed. This is clearly beneficial for performing both reconstruction and registration tasks. We show examples of this approach on active contours and deformable surfaces. Furthermore, a global transformation based on axial symmetry is introduced.