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eScience workflows use orchestration for integrating and coordinating distributed and heterogeneous scientific resources, which are increasingly exposed as web services. The rate of growth of scientific data makes eScience workflows data‐intensive, challenging existing workflow solutions. Efficient methods of handling large data in scientific workflows based on web services are needed. The purpse of this paper is to address this issue.

In a previous paper the authors proposed Data‐Flow Delegation (DFD) as a means to optimize orchestrated workflow performance, focusing on SOAP web services. To improve the performance further, they propose pipelined data‐flow delegation (PDFD) for web service‐based eScience workflows in this paper, by leveraging from the domain of parallel programming. Briefly, PDFD allows partitioning of large datasets into independent subsets that can be communicated in a pipelined manner.

The results show that the PDFD improves the execution time of the workflow considerably and is capable of handling much larger data than the non‐pipelined approach.

Execution of a web service‐based workflow hampered by the size of data can be facilitated or improved by using services supporting Pipelined Data‐Flow Delegation.

Contributions of this work include the proposed concept of combining pipelining and Data‐Flow Delegation, an XML Schema supporting the PDFD communication between services, and the practical evaluation of the PDFD approach.

The aim of this paper is to review the concept of the virtual research environment (VRE) in the light of its development over the past five years, and assess its applicability to the arts and humanities disciplines.

Evidence from a number of case studies exemplifying the VRE approach is reviewed, and the case of a VRE in archaeology, the Silchester Roman Town project, is discussed in detail. The interpretive implications of using computers as a means of dealing with artistic and humanistic data, are highlighted.

There is a critical comparison to be drawn between VREs in the sciences and the humanities/arts. This is caused by the “fuzzy” nature of data and workflows in the latter, as compared with the more formal and definable research practice in the former. It is proposed that, to deal with this, the plan of any project which seeks to set up a VRE in the humanities should consider the research process under three headings: processes which the VRE seeks to introduce, existing processes which it seeks to undertake digitally, and processes which will be unaffected by the VRE.

In order to progress “the VRE” from being an artificial construct, driven by dedicated project funding, towards being an embedded part of research practice, those concerned with developing VREs need to consider the nuances of those research practices. This paper seeks to review those nuances by synthesizing data and experience from existing projects, thereby facilitating that embedding process.

The purpose of this article is to give an overview of the development of a Virtual Research Environment (VRE) conceptual model for the management of research data at a South African university.

The research design of this article consists of empirical and non-empirical research. The non-empirical part consists of a critical literature review to synthesise the strengths, weaknesses (limitations) and omissions of identified VRE models as found in literature to develop a conceptual VRE model. As part of the critical literature review concepts were clarified and possible applications of VREs in research lifecycles and research data lifecycles were explored. The empirical part focused on the practical application of this model. This part of the article follows an interpretivist paradigm, and a qualitative research approach, using case studies as inquiry method. Case studies with a positivist perspective were selected through purposive sampling, and inferences were drawn from the sample to design and test a conceptual VRE model, and to investigate the management of research data through a VRE. Investigation was done through a process of participatory action research (PAR) and included semi-structured interviews and participant observation data collection techniques. Evaluation of findings was done through formative and summative evaluation.

The article presents a VRE conceptual model, with identified generic component layers and components that could potentially be applied and used in different research settings/disciplines. The article also reveals the role that VREs play in the successful management of research data throughout the research lifecycle. Guidelines for setting up a conceptual VRE model are offered.

This article assisted in clarifying and validating the various components of a conceptual VRE model that could be used in different research settings and disciplines for research data management.

This article confirms/validates generic layers and components that would be needed in a VRE by synthesising these in a conceptual model in the context of a research lifecycle and presents guidelines for setting up a conceptual VRE model.

Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance.

A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed.

The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches.

This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

The concept of business process (BP) as a service is a new solution in enterprises for the purpose of using specific BPs. BPs represent combinations of software services that must be properly executed by the resources provided by a company’s information technology infrastructure. As the policy requirements are different in each enterprise, processes are constantly evolving and demanding new resources in terms of computation and storage. To support more agility and flexibility, it is common today for enterprises to outsource their processes to clouds and, more recently, to cloud federation environment. Ensuring the optimal allocation of cloud resources to process service during the execution of workflows in accordance with user policy requirements is a major concern. Given the diversity of resources available in a cloud federation environment and the ongoing process changes required based on policies, reallocating cloud resources for service processing may lead to high computational costs and increased overheads in communication costs.

This paper presents a new adaptive resource allocation approach that uses a novel algorithm extending the natural-based intelligent water drops (IWD) algorithm that optimizes the resource allocation of workflows on the cloud federation which can estimate and optimize final deployment costs. The proposed algorithm is implemented and embedded within the WokflowSim simulation toolkit and tested in different simulated cloud environments with different workflow models.

The algorithm showed noticeable enhancements over the classical workflow deployment algorithms taking into account the challenges of data transfer. This paper made a comparison between the proposed IWD-based workflow deployment (IWFD) algorithm with other proposed algorithms. IWFD presented considerable improvements in the makespan, cost and data transfer in most situations in the cloud federation environment.

An extension for WorkflowSim to support the implementation of BPs in a federation cloud space regarding BP policy. Optimize workflow execution performance in Federated clouds by means of IWFD algorithm.

This paper is a qualitative study of how social sciences faculty construct their research workflows with the help of technological tools. The purpose of this study is to examine faculty scholarly workflows and how both tools and practices support the research process. This paper could inform academic libraries on how to support scholars throughout the research process.

This is a qualitative study case study of ten faculty members from six research universities from the United States and Canada. Semi-structured interviews were conducted and recorded. Atlas.ti was used to code and analyze the transcripts; each participant was a separate case. Descriptive coding was used to identify digital tools used for collaboration; process and descriptive coding was utilized to examine practices in scholarly workflows.

Through case study analysis the results of this study include the role of technology in faculty research workflows. Each workflow was grouped into four categories: information literacy, information management, knowledge management, and scholarly communication. The findings included scholars creating simple workflows for efficiency and collaboration and utilizing workarounds.

The study did not observe faculty in the process of doing research and, thus, only reports on what the researchers say that they do.

The research is unique in that there is almost no research on how social scientists conduct their research workflows and the affordances/impasses of this process.

The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community.

The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses.

The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases.

The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

This paper aims to present an evaluation of open source OCR for supporting research on material in small‐ to medium‐scale historical archives.

The approach was to develop a workflow engine to support the easy customisation of the OCR process towards the historical materials using open source technologies. Commercial OCR often fails to deliver sufficient results here, as their processing is optimised towards large‐scale commercially relevant collections. The approach presented here allows users to combine the most effective parts of different OCR tools.

The authors demonstrate their application and its flexibility and present two case studies, which demonstrate how OCR can be embedded into wider digitally enabled historical research. The first case study produces high‐quality research‐oriented digitisation outputs, utilizing services that the authors developed to allow for the direct linkage of digitisation image and OCR output. The second case study demonstrates what becomes possible if OCR can be customised directly within a larger research infrastructure for history. In such a scenario, further semantics can be added easily to the workflow, enhancing the research browse experience significantly.

There has been little work on the use of open source OCR technologies for historical research. This paper demonstrates that the authors' workflow approach allows users to combine commercial engines' ability to read a wider range of character sets with the flexibility of open source tools in terms of customisable pre‐processing and layout analysis. All this can be done without the need to develop dedicated code.

The purpose of this paper is to present a four-level architecture that aims at integrating, publishing and retrieving ecological data making use of linked data (LD). It allows scientists to explore taxonomical, spatial and temporal ecological information, access trophic chain relations between species and complement this information with other data sets published on the Web of data. The development of ecological information repositories is a crucial step to organize and catalog natural reserves. However, they present some challenges regarding their effectiveness to provide a shared and global view of biodiversity data, such as data heterogeneity, lack of metadata standardization and data interoperability. LD rose as an interesting technology to solve some of these challenges.

Ecological data, which is produced and collected from different media resources, is stored in distinct relational databases and published as RDF triples, using a relational-Resource Description Format mapping language. An application ontology reflects a global view of these datasets and share with them the same vocabulary. Scientists specify their data views by selecting their objects of interest in a friendly way. A data view is internally represented as an algebraic scientific workflow that applies data transformation operations to integrate data sources.

Despite of years of investment, data integration continues offering scientists challenges in obtaining consolidated data views of a large number of heterogeneous scientific data sources. The semantic integration approach presented in this paper simplifies this process both in terms of mappings and query answering through data views.

This work provides knowledge about the Guanabara Bay ecosystem, as well as to be a source of answers to the anthropic and climatic impacts on the bay ecosystem. Additionally, this work will enable evaluating the adequacy of actions that are being taken to clean up Guanabara Bay, regarding the marine ecology.

Mapping complexity is traded by the process of generating the exported ontology. The approach reduces the problem of integration to that of mappings between homogeneous ontologies. As a byproduct, data views are easily rewritten into queries over data sources. The architecture is general and although applied to the ecological context, it can be extended to other domains.

This paper aims to propose a scheduling technique for parameter sweep workflows, which are used in parametric study and optimization. When executed in multiple parallel instances in the grid environment, it is necessary to address bottleneck and load balancing to achieve an efficient execution.

A bottleneck detection approach is based on commonly known performance metrics of grid resources. To address load balancing, a resource requirement similarity metric is introduced to determine the likelihood of the distribution of tasks across available grid resources, which is referred to as an execution context. The presence of a bottleneck and the execution context are used in the main algorithm, named ABeC, to schedule tasks selectively at run-time to achieve a better overall execution time or makespan.

According to the results of the simulations against four existing algorithms using several scenarios, the proposed technique performs, at least, similarly to the existing four algorithms in most cases and achieves better performance when scheduling workflows have a parallel structure.

The bottleneck detection and the load balancing proposed in this paper require only common resource and task information, rendering it applicable to most workflow systems. The proposed scheduling technique, through such selective behaviour, may help reduce the time required for the execution of multiple instances of a grid workflow that is to be executed in parallel.