This research introduces a customizable proxy to enhance the security and governance of Apache Kafka. The proxy uses TransformNode, DecisionNode, and ObserverNode controls to manage data flows without modifying existing client applications. Initial tests show negligible latency impact, validating its effectiveness in improving Kafka’s security and governance capabilities while maintaining system efficiency.

In the context of 6G, Digital Twin technology has the potential to play a crucial role by offering a sophisticated, dynamic model that mirrors the physical world in real-time. Integrating digital twins into 6G enables precise orchestration and optimization of network resources, meeting the diverse and stringent demands of next-generation applications. Furthermore, digital twins provide a platform for continuous learning and AI-driven analysis. Research on the architecture of Network Digital Twins (NDTs) for 5G/6G is still limited, often focusing on partial implementations rather than comprehensive, full-stack approaches. This paper proposes a high-level architectural framework for 6G NDTs system, structured across three layers: the Physical Twin Layer, Digital Twin Layer, and Application and Service Layer. We discuss the challenges of deploying these systems and the importance of selecting appropriate tools, presenting an experimental case study that demonstrates the impact of different tool choices on system performance. Our findings underscore the need for flexible, scalable solutions to fully realize the benefits of NDTs in 6G networks.

We report on the ideas and experiences of adapting Brane, a workflow execution framework, for use cases involving medical data exchange and processing. These use cases impose new requirements on the system to enforce policies encoding safety properties, ranging from access control to legal regulations pertaining to data privacy. Our approach emphasizes users' control over the extent to which they cooperate in distributed execution, at the cost of revealing information about their policies.

On the road towards personalised medicine, one of the main challenges is to enforce security and network low-level policies to secure data-sharing. The proposed dynamic framework defines the topology of the service chains to enforce network and security policies by instantiating Virtual Network Functions (VNF's) on the fly via light-weight and easily-deployable containers. In this paper, we profile the resource utilisation of chained VNF's deployed to enable data movement within different healthcare use cases. We provide example configurations that map to a couple of use cases (e-Health record query and heath data streaming), then we monitor and collect CPU utilisation of the different VNF compositions. In the considered policies we can: discard flow, protect (encrypt) and transmit, or allow with no protection. To enforce each policy, we deploy a firewall function (relatively heavy-weight function), encryption function, and a decryption function (light-weight stream cipher). As a result, we analyse the behaviour of the VNF services with various setups, and then we aim to further use this analysis to build the placement heuristic according to available and trusted clusters resources. Subsequently, we will recommend heuristic-based placement based on collected profiling data of the resource usage and limits for high availability, optimal performance, and minimal resource waste.

Utilising programmable infrastructures is a promising approach to support secure data-sharing across healthcare domains of different capabilities in terms of network and security. The EPI1(Enabling Personalised Interventions) framework automates the setup of the underlying infrastructure while considering different requirements communicated by the EPI components, such as logic area generator and policy management system.In our approach to dynamically provide collaborative environments, we containerise network functions (VFs), that are shipped out and instantiated at the edge of the network. We use container-based Virtual Network Function (VNFs) to accomplish fast deployment, high reusability, and low-performance overhead.Traffic interception and redirection through the chain of containerised network functions is a core feature of our framework. In this paper, we focus on the implementation and design of this tool for packet interception and redirection. We evaluate the performance of two approaches: an NGINX-based reverse proxy method and a SOCKS protocol-based method. We benchmark them to determine the overhead compared to a direct data-sharing session with no proxy. We conclude that the reverse proxy performs better in terms of overhead. Nonetheless, the SOCKS-based method works on a lower network level support all traffic types and offer a higher processing rate. We compare the methods according to other performance parameters. Subsequently, the choice of method will depend on the application performance requirements.

Regardless of the context and rationale, running distributed applications on geographically dispersed IT resources often comes with various technical and organizational challenges. If not addressed appropriately, these challenges may impede development, and in turn, scientific and business innovation. We have developed the Brane framework to support implementers in addressing these challenges. Brane utilizes containerization to encapsulate functionalities as portable building blocks. Through programmability, application orchestration can be expressed using an intuitive domain-specific language. As a result, end-users with limited programming experience are empowered to compose applications by themselves, without having to deal with the underlying technical details. They can do this from user-friendly interactive notebooks. In this paper, we introduce Brane, describe its components and features, and validate the framework with an implementation of a real-world scientific use case.

Summary Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many‐task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low‐complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many‐task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.

Privacy considerations obligate careful and secure processing of personal data. This is especially true when personal data is linked against databases from other organizations. During such endeavours, privacy-preserving record linkage (PPRL) can be utilized to prevent needless exposure of sensitive information to other organizations. With the increase of personal data that is being gathered and analyzed, scalable PPRL capable of handling massive databases is much desired. In this work, we evaluate Apache Spark as an option to scale PPRL. Not only is it valuable to have a scalable PPRL implementation, but one based on the Spark would also be commonly deployable and could take advantage of further development of the ecosystem. Our results show that a PPRL solution based on Spark outperforms alternatives when it comes to handling multiple millions of records; can scale to dozens of nodes; and is on-par with regular record linkage implementations in terms of achieved results.

While political commitments for building exascale systems have been made, turning these systems into platforms for a wide range of exascale applications faces several technical, organisational and skills-related challenges. The key technical challenges are related to the availability of data. While the first exascale machines are likely to be built within a single site, the input data is in many cases impossible to store within a single site. Alongside handling of extreme-large amount of data, the exascale system has to process data from different sources, support accelerated computing, handle high volume of requests per day, minimize the size of data flows, and be extensible in terms of continuously increasing data as well as increase in parallel requests being sent. These technical challenges are addressed by the general reference exascale architecture. It is divided into three main blocks: virtualization layer, distributed virtual file system, and manager of computing resources. Its main property is modularity which is achieved by containerization at two levels: 1) application containers - containerization of scientific workflows, 2) micro-infrastructure - containerization of extreme-large data service-oriented infrastructure. The paper also presents an instantiation of the reference architecture - the architecture of the PROCESS project (PROviding Computing solutions for ExaScale ChallengeS) and discuss its relation to the reference exascale architecture. The PROCESS architecture has been used as an exascale platform within various exascale pilot applications. This work will present the requirements and the derived architecture as well as the 5 use cases pilots that it made possible.

Machine learning models recently have seen a large increase in usage across different disciplines. Their ability to learn complex concepts from the data and perform sophisticated tasks combined with their ability to leverage vast computational infrastructures available today have made them a very attractive choice for many challenges in academia and industry. In this context, deep Learning as a sub-class of machine learning is specifically becoming an important tool in modern computing applications. It has been successfully used for a wide range of different use cases, from medical applications to playing games. Due to the nature of these systems and the fact that a considerable portion of their use-cases deal with large volumes of data, training them is a very time and resource consuming task and requires vast amounts of computing cycles. To overcome this issue, it is only natural to try to scale deep learning applications to be able to run them across in order to achieve fast and manageable training speeds while maintaining a high level of accuracy. In recent years, a number of frameworks have been proposed to scale up ML algorithms to overcome the scalability issue, with roots both in the academia and the industry. With most of them being open source and supported by the increasingly large community of AI specialists and data scientists, their capabilities, performance and compatibility with modern hardware have been honed and extended. Thus, it is not easy for the domain scientist to pick the tool/framework best suited for their needs. This research aims to provide an overview of the relevant, widely used scalable machine learning and deep learning frameworks currently available and to provide the grounds on which researchers can compare and choose the best set of tools for their ML pipeline.

Applications and infrastructures are increasingly becoming more complex. Infrastructures have several layers of virtualisation, programmability and management while scientific applications are diverse in their computing model archetypes. Mapping these two opposing ends of the stack is a non-trivial task. Here we propose a new programming paradigm and architecture that takes into account the different layers of the stack in an effort to create isolated, portable and scalable application micro-infrastructures.

In this paper we discuss our efforts in "unlocking" the Long Term Archive (LTA) of the LOFAR radio telescope. This is a large (> 43 PB) archive that expands with about 7 PB per year by the ingestion of new observations. It consists of coarsely calibrated "visibilities", i.e. correlations between signals from LOFAR stations. Currently, only a small fraction of the LOFAR LTA consists of sky maps, which are needed for most astronomical research. Unfortunately, creating such sky maps can be challenging, due to the data sizes of the observations and the complexity and compute requirements of the software involved. We try to fix this by enabling a simple one-click-reduction of LOFAR observations into sky maps for any user of this archive. This work was performed as part of the PROCESS 1 project, which aims to provide generalizable open source solutions for user friendly exascale data processing.

Summary During the last several years, we have observed an exponential increase in the demand for Data Scientists in the job market. As a result, a number of trainings, courses, books, and university educational programs (both at undergraduate, graduate and postgraduate levels) have been labeled as “Big data” or “Data Science”; the fil‐rouge of each of them is the aim at forming people with the right competencies and skills to satisfy the business sector needs. In this paper, we report on some of the exercises done in analyzing current Data Science education offer and matching with the needs of the job markets to propose a scalable matching service, ie, COmpetencies ClassificatiOn (E‐CO‐2), based on Data Science techniques. The E‐CO‐2 service can help to extract relevant information from Data Science–related documents (course descriptions, job Ads, blogs, or papers), which enable the comparison of the demand and offer in the field of Data Science Education and HR management, ultimately helping to establish the profession of Data Scientist.

Data manipulation is often named as a serious threat to data integrity. Data can be tampered with, and malicious actors could use this to their advantage. Data users in various application domains want to be ensured that the data they are consuming are accurate and have not been tampered with. To validate the integrity of these data, we describe a blockchain-based hash validation method. The method assumes that the actual data is stored separately from the blockchain, and then allows a data identifier and a hash of these data to be submitted to the blockchain. The actual data can be validated against the hash on the blockchain at any time. Several use cases are described for blockchain-based hash validation, and to validate the method it is implemented inside an application audit trail to validate the audit trail data. This implementation shows that blockchain-based hash validation is able to detect malicious and accidental changes that were made to the data.

With the increasing amount of data the importance of data analysis has grown. A large amount of this data has shifted to cloud-based storage. The cloud offers storage and computation power. The Cookery framework is a tool developed to build application in the cloud for scientists without a complete understanding of programming. In this paper with present the cookery systems and how it can be used to authenticate and use standard online 3rd party services to easily create data analytics pipeline. Cookery framework is not limited to work with standard web services, it can also integrate and work with the emerging AWS Lambda. The combination of AWS Lambda and Cookery, which makes it possible for people, who do not have any program experience, to create data processing pipeline using cloud services in short time.

Data Science is an emerging field of science, which requires a multi-disciplinary approach and is based on the Big Data and data intensive technologies that both provide a basis for effective use of the data driven research and economy models. Modern data driven research and industry require new types of specialists that are capable to support all stages of the data lifecycle from data production and input to data processing and actionable results delivery, visualisation and reporting, which can be jointly defined as the Data Science professions family. The education and training of Data Scientists currently lacks a commonly accepted, harmonized instructional model that reflects all multi-disciplinary knowledge and competences that are required from the Data Science practitioners in modern, data driven research and the digital economy. The educational model and approach should also solve different aspects of the future professionals that includes both theoretical knowledge and practical skills that must be supported by corresponding education infrastructure and educational labs environment. In modern conditions with the fast technology change and strong skills demand, the Data Science education and training should be customizable and delivered in multiple form, also providing sufficient data labs facilities for practical training. This paper discussed both aspects: building customizable Data Science curriculum for different types of learners and proposing a hybrid model for virtual labs that can combine local university facility and use cloud based Big Data and Data analytics facilities and services on demand. The proposed approach is based on using the EDISON Data Science Framework (EDSF) developed in the EU funded Project EDISON and CYCLONE cloud automation systems being developed in another EU funded project CYCLONE.

This paper presents the Data Science Model Curriculum (MC-DS) that is based on the Data Science Competence Framework and Data Science Body of Knowledge defined in EDISON Data Science Framework (EDSF). MC-DS follows a competence-based curriculum design approach grounded in the Data Science competences (CD-DS) defined in EDSF and correspondingly defined Learning Outcomes (LO). The DSBoK provides a basis for structuring the proposed MC-DS by Knowledge Area Groups (KAG) defined in correspondence with the CF-DS competence groups. ECTS point allocation to specific areas is recommended for Master's and Bachelor's program covering professional profile groups.

Data Science is an emerging field of science, which requires a multi-disciplinary approach and should be built with a strong link to emerging Big Data and data driven technologies, and consequently needs re-thinking and re-design of both traditional educational models and existing courses. The education and training of Data Scientists currently lacks a commonly accepted, harmonized instructional model that reflects by design the whole lifecycle of data handling in modern, data driven research and the digital economy. This paper presents the EDISON Data Science Framework (EDSF) that is intended to create a foundation for the Data Science profession definition. The EDSF includes the following core components: Data Science Competence Framework (CF-DS), Data Science Body of Knowledge (DS-BoK), Data Science Model Curriculum (MC-DS), and Data Science Professional profiles (DSP profiles). The MC-DS is built based on CF-DS and DS-BoK, where Learning Outcomes are defined based on CF-DS competences and Learning Units are mapped to Knowledge Units in DS-BoK. In its own turn, Learning Units are defined based on the ACM Classification of Computer Science (CCS2012) and reflect typical courses naming used by universities in their current programmes. The paper provides example how the proposed EDSF can be used for designing effective Data Science curricula and reports the experience of implementing EDSF by the Champion Universities that cooperate with the EDISON project.

Data Science is becoming a field connecting multi-year development in areas such as Big Data and Data Analytics, and also applied domains like Bioengineering. Data Science education programs are rapidly being created on all levels. Usually it happens through reuse or renaming and can result in curricula that lack proper balance of competences, which balance is necessary for future data scientists. Our quantitative analysis of over 300 programs worldwide shows that at least one of the three core data science competence groups is under-represented in the majority of programs. Moreover, general business courses are often suggested to students to cover the domain competence group, which in most cases results in superficial treatment of this competence group. Our further qualitative analysis demonstrates that learning outcomes for most of the courses are usually not defined or defined improperly.

This paper presents results and experience by the authors based on the few delivered courses on Cloud Computing for different target groups of students, specialists and trainees. The developed courses implement the proposed by the authors instructional methodology integrating the two major concepts of effective learning: the Bloom's Taxonomy of cognitive learning processes and Andragogy as the adult learning methodology. The central part of the proposed approach is the Common Body of Knowledge in Cloud Computing (CBK-CC) that defines the professional level of knowledge in the selected domain and allows consistent curricula structuring and profiling. The paper presents the structure of the courses and explains the principles used for developing course materials, such as Bloom's Taxonomy applied for technical education, and andragogy instructional model for professional education and training. The developed courses are based on the well-defined Cloud Computing architecture, service and operational model, and stakeholder roles/responsibilities. The paper provides a short description of the developed education and training courses on Cloud Computing that illustrate how the proposed CBK-CC and instructional methodologies are used in different learning environments and for different learners' groups.

This paper presents a methodology used to create a site independent assessment process of the capabilities of service management systems in federated e-infrastructures that can contribute to introduce or improve service management in these application domains. Based on ISO/IEC 20000 concepts it consist of an actors and relationships model, a set of management processes with their corresponding requirements and a capability model that all converge in an easy to use assessment tool. The methodology has been evaluated through its adoption in an existing federated e-infrastructure.

SUMMARY Task farming is often used to enable parameter sweep for exploration of large sets of initial conditions for large scale complex simulations. Such applications occur very often in life sciences. Available solutions enable to perform parameter sweep by creating multiple job submissions with different parameters. This paper presents an approach to farm workflows, employing service oriented paradigms using the WS‐VLAM workflow manager, which provides ways to create, control, and monitor workflow applications and their components. We present two service‐oriented approaches for workflow farming: task level, whereby task harness acts as services by being invoked on which task to load, and data level, where the actual task is invoked as a service with different chunks of data to process. An experimental evaluation of the presented solution is performed with a biomedical application for which 3000 simulations were required to perform a Monte Carlo study. Copyright © 2013 John Wiley & Sons, Ltd.

This paper presents ongoing work to develop advanced education and training course on the Cloud Computing technologies foundation and engineering by a cooperating group of universities and the professional education partners. The central part of proposed approach is the Common Body of Knowledge in Cloud Computing (CBK-CC) that defines the professional level of knowledge in the selected domain and allows consistent curricula structuring and profiling. The paper presents the structure of the course and explains the principles used for developing course materials, such as Bloom's Taxonomy applied for technical education, and andragogy instructional model for professional education and training. The paper explains the importance of using the strong technical foundation to build the course materials that can address interests of different categories of stakeholders and roles/responsibilities in the Cloud Computing services provisioning and operation. The paper provides a short description of summary of the used Cloud Computing related architecture concepts and models that allow consistent mapping between CBK-CC, stakeholder roles/responsibilities and required skills, explaining also importance of the requirements engineering stage that provides a context for cloud based services design. The paper refers to the ongoing development of the educational course on Cloud Computing at the University of Amsterdam, University of Stavanger and provides suggestions for building advanced online training course for IT professionals.

Cyber physical systems, such as intelligent dikes and smart energy systems, require scalable and flexible computing infrastructures to process data from instruments and sensor networks. Infrastructure as a Service clouds provide a flexible way to allocate remote distributed resources, but lack mechanisms to dynamically configure software (dependencies) and manage application execution. This paper describes the design and implementation of the Intercloud Operating System (ICOS), which acts between applications and distributed clouds, i.e., the Intercloud. ICOS schedules, configures, and executes applications in the Intercloud while taking data dependencies, budgets, and deadlines into account. Based on our experiences with the prototype, we present considerations and additional research challenges. The research on ICOS clarifies essential concepts needed to realize a flexible and scalable on-demand execution platform for distributed applications over distributed cloud providers.

The multitude of scientific services and processes being developed brings about challenges for future in silico distributed experiments. Choosing the correct service from an expanding body of processes means that the the task of manually building workflows is becoming untenable. In this paper we propose a framework to tackle the future of scientific collaborative distributed computing. We introduce the notion of Networked Open Processes whereby processes are exposed, published, and linked using semantics in the same way as is done with Linked Open Data. As part of the framework we introduce several novel concepts including Process Object Identifiers, Semantic Function Templates, and TReQL, a SQL-like language for querying networked open process graphs.

MapReduce frameworks proved to be a good solution for storing and processing large amounts of data. Thanks to data parallelism, they allow to move computations very close to the storage and therefore to reduce an influence of "I/O bottleneck". Workflow Management Systems, in turn, are widely used for modeling of scientific applications. Users that are willing to use MapReduce frameworks in their workflows have to run separate environment to develop Map/Reduce operations. In this paper we propose an approach that will allow to extend existing application models by MapReduce routines. Our solution bases on DSL constructed on top of Ruby programming language. It follows examples of Sawzall and Pig Latin languages and allows to define Map/Reduce operations in minimalist way. Moreover, because the language is based on Ruby, the model allows to use user defined routines and existing Ruby libraries. A particular model of the workflow management system can be extended with our DSL letting users to use one environment for developing the workflow and MapReduce application.

SUMMARY Ontology development involves people with different background knowledge and expertise. It is an elaborate process, where sophisticated tools for experienced knowledge engineers are available. However, domain experts need simple tools that they can use to focus on ontology instantiation. In this paper, we propose a methodology with a separation of concern between domain experts and knowledge engineers. This separation allows domain experts to focus on information processing and ontology instantiation while providing immediate feedback to the knowledge engineers on usability of the ontology being developed. We have designed and implementedOINTED, an adaptive online ontology instance editor that supports this methodology. We present usage examples ofOINTEDthat highlight three main features: the intuitive visualization of concepts, instances, and relationships within an ontology; the seamless integration in pre‐existing problem solving environment; and the assistance in ontology evolution.OINTEDcomplements existing tools suited for knowledge engineers by enabling immediate feedback and a shorter ontology development life cycle. Copyright © 2012 John Wiley & Sons, Ltd.

Many e-Science applications are increasingly relying on orchestrating workflows of static web services. The static nature of these web services means that workflow management systems have no control over the underlying mechanics of such services. This lack of control manifests itself as a problem when optimizing workflow execution since techniques such as data-locality aware deployment and service-to-service communication are very difficult to achieve. In this paper we propose a novel approach for mobilizing scientific web services onto common distributed resources and as such enable back-to-back communication between cooperating web services, autonomous web service scaling through fuzzy control and autonomous web service workflow orchestration.

The amount of computing resources currently available on Clouds is large and easily available with pay per use cost model. E-Science applications that need on-demand execution benefit from Clouds, because no permanent computing resources to support peak demand has to be acquired. In this paper, we present AMOS, a system that automates creation and management of temporary Grids on a Cloud to execute (parts of) application workflows. We performed experiments with AMOS and a representative e-Science application on a research Grid and on the Amazon EC2 Cloud. The results show that AMOS is a viable approach to manage and execute e-Science applications in a flexible Grid environment and to explore novel mechanisms that allow optimal utilization of Cloud resources. Furthermore, we consider AMOS as a step towards an operating system for (virtual) infrastructures that enables Grid applications to control their computational resources at run-time.

Grid workflow management systems automate the orchestration of scientific applications with large computational and data processing needs, but lack control over network resources. Consequently, the management system cannot prevent multiple communication intensive applications to compete for network resources, which leads to unpredictable performance. Currently, the lack of control over network resources may prevent certain applications, i.e. applications that need high capacity and Quality of Service, to utilize Grids. Hence, such applications would use dedicated infrastructures. Because the costs to build dedicated infrastructures may far exceed the cost of using existing Grids, the Grid needs to support mechanisms to optimize the interworking between networks and applications. In this paper, we present the architecture and proof of concept to control network resources from Grid workflow management system and to manage network resources from workflow-enabled applications at run-time. Depending on the current network infrastructure capabilities or future advances, applications may employ existing QoS mechanisms or use application-specific ones to provide the desired network service. We believe that our approach leads to performance improvements in communication intensive applications and enables novel Grid applications, which require optimal interworking between networks and applications.

Summary form only given. A typical parameter sweep application (PSA) is a parameterized application which has to be executed independently large number of times, to locate a particular point in the parameter space that satisfies certain criteria. From the perspective of domain scientists, the complexity of underlying grid environment should be hidden so that domain scientists can focus on their main concern on performing their experiments. The system needs to provide friendly user environment for scientists to change parameter space or set new policy for execution at runtime. The framework should provide flexible interface for porting legacy applications to a PSA. We proposed five functional components for the interactive framework: a GUI for user to describe experiment, a visualizer to presenting computing results, a coordinator to schedule the execution of computing tasks, a repository to collect computing results, and interface to job scheduling tools, e.g., from Grid. In the current prototype, a tuple space like workspace is used to maintain state of experiment. This prototype allows basic interactivity such as modification of experiment state during execution which demonstrated the capability to manipulate parameter sweep tasks at run time.

A significant challenge for Web services is to find reliable and efficient methods to transfer large data between them. This paper describes the problem of scalable data transport between Web services, and proposes a solution: the development of a modular server/client library that uses SOAP as a control channel while the actual data transport is accomplished by various protocol implementation. Apart from file transport, the proposed approach offers the facility of direct data streaming between Web services, an approach that could benefit workflow execution time.

Recent advances in Internet and Grid technologies have greatly enhanced processes in scientific experiments; not only computing and data intensive tasks become feasible, but also large scale collaborations between resources and users are now possible. Scientific workflows encode intelligence of successful experiments and become important resources. Sharing these resources and allowing scientists to cooperate in one experiment are essential to promote the knowledge transfer among scientists and to accelerate the scientific achievements. In this demo, we present a solution developed in the Dutch Virtual Laboratory for e-Science project for supporting cooperative experiments.

The development of Medical Image Analysis (MIA) applications that can successfully be applied in clinical practice is difficult for several reasons, one of them being the large amount and variety of resources involved (people, data, methods, computing). The application goes through several phases (development, parameter optimization, evaluation and clinical deployment) usually supported by different systems. The lack of support for information flow from phase to phase puts extra logistics burden on the lifecycle of MIA applications. In this paper we describe our first efforts to develop a Problem Solving Environment (PSE) for MIA applications using the three systems available at the proof-of-concept environment of the Virtual Laboratory for e-Sciences project. The proposed PSE implements data provenance mechanisms that support information flow among systems, facilitating navigation across phases of the application lifecycle.

Most of the existing scientific workflow management systems (SWMS) are driven by applications from specific domains and are developed in academic projects. It is challenging to introduce an existing SWMS to a new domain; not only the workflow model and description language do not easily fit in new problem domains, but also the unstable development state of existing systems does not provide all functionality required by the new applications and thus gives high risk for the development. Aggregating different workflow systems as one generic environment enables the sharing on both components and processes between experiments, and promotes the knowledge transfer between domains. A workflow bus approach is to integrate different e-science workflow engines via a software bus. In this paper, we present the basic idea of workflow bus, and discuss how Jade agent framework can be used to prototype the runtime infrastructure of a workflow bus.

The usage of ontologies to develop a semantically rich experiment models promises to be a key advantage of scientific applications over earlier alternatives. Whilst it is often recognized that information gathered for the ontology modeling process can describe naturally the scientific knowledge and can be used for interoperation among heterogeneous systems (by establishing a global schema, for instance), it may also be used to create data structures, including database schema and initial code signatures, containing metadata and semantics for their applications. The aspects involved in the translation of ontology models into suited metadata, however, can render in wasted efforts and useless schemas for scientific applications. This paper explores an approach to generate semiautomatically appropriate data structures for handling scientific information. Based on this approach, we developed a tool that let scientists to develop canonical models and automatically generate the related database schema. This tool supports a wide range of scientific use cases for complex models within the VL-e project. This project carries out concerted research along the complete e-science technology chain, ranging from applications to networking, focusing on new methodologies and re-usable components

Explorative research is a vital part of biological sciences. Biologists frequently have to examine and compare multiple (large) sets of biological data in an interactive and explorative manner. Exploring alternative ways of examining the data and managing the necessary resources often require substantial (manual) effort and time. In this paper, we present a concrete example of how VLAM, a grid-based workflow management system, can enhance experimentation. We discuss in detail the process of developing the SigWin-detector, an application in the domain of bioinformatics. We show that SigWin-detector can promptly identify regions of increased gene expression in transcriptome maps and periodicity in weather data. We also show that the workflow can be extended or partially modified. The individual modules can also be used to compose different experiments. SigWin-detector fulfills the requirements of interactive and explorative experimentation.

Grid technology can offer a powerful infrastructure for a broad spectrum of (scientific) application areas, but the uptake of grids by "real' applications has been slow. Several aspects contribute to this scenario, among them a large gap between the communities that develop and use the technology. While grid developments focus mostly on functionality, usability issues are also very important for the endusers. In the VL-e project, one of the goals is to bridge this gap, bringing (existing) grid technology to end-users in various application domains. This paper describes the current attempts to provide an IT infrastructure to facilitate neuroimaging research, in particular functional Magnetic Resonance Imaging (fMRI), which enables the study of brain function. The infrastructure offers resources for fMRI data acquisition, data storage, computing and services. All (local and grid) resources are accessed directly by the users from a virtual desktop implemented by a Virtual Resource Browser (VBrowser).

In e-Science, a Grid environment enables data and computing intensive tasks and provides a new supporting infrastructure for scientific experiments. Scientific workflow management systems (SWMS) hide the integration details among Grid resources and allow scientists to prototype an experimental computing system at a high level of abstraction. However, the development of an effective SWMS requires profound knowledge on both application domains and the network programming, and is often time consuming and domain specific. Integrating mature implementations of domain specific SWMS improves reusability of workflow resources and promotes a generic framework for different e-Science domains. In this paper, we discuss different options to derive a generic workflow management system from domain specific implementations, and propose a workflow bus based solution, called VLE-WFBus. Legacy SWMSs are wrapped as federated components and are loosely coupled as one workflow system via a runtime infrastructure. An agent based prototype is presented; the integration among different workflow management systems has been demonstrated.

In e-science environments, scientific workflow management systems (SWMS) hide the integration details among grid resources and allow scientists to prototype an experimental computing system at a high level of abstraction. However, the development of an effective SWMS requires profound knowledge on both application domains and the network programming, and is often time consuming. Agent technologies provide suitable solutions to decompose the control intelligence of flow execution and to encapsulate distributed e-science resources. The work presented in this paper is conducted in the context of the Dutch Virtual Laboratory for e-science (VL-e) project. Agent technologies are proposed to realize generic workflow support.

Achieving reliable implementation of a real-time speaker independent speech recognizer through the telephone network is a challenging research problem. Besides requiring the selection of a suitable algorithm, which takes into account both real-time and accuracy constrains, it requires also adequate hardware architecture and optimized software. This paper presents a dedicated multiprocessor DSP architecture for telecom applications. Based on ADSP-21060 SHARC DSPs, it is the kernel of a digital interactive voice response (IVR) system that is connected to a digital switch through the primary CCITT standard time division multiplexing line of 2.048 Mbps. We attempt to show how a multi-DSP based hardware can be designed for a specific problem in telecommunication, along with the implementation of automatic speech recognition (ASR) to the digital IVR system.