11,210 research outputs found
Enabling Interactive Analytics of Secure Data using Cloud Kotta
Research, especially in the social sciences and humanities, is increasingly
reliant on the application of data science methods to analyze large amounts of
(often private) data. Secure data enclaves provide a solution for managing and
analyzing private data. However, such enclaves do not readily support discovery
science---a form of exploratory or interactive analysis by which researchers
execute a range of (sometimes large) analyses in an iterative and collaborative
manner. The batch computing model offered by many data enclaves is well suited
to executing large compute tasks; however it is far from ideal for day-to-day
discovery science. As researchers must submit jobs to queues and wait for
results, the high latencies inherent in queue-based, batch computing systems
hinder interactive analysis. In this paper we describe how we have augmented
the Cloud Kotta secure data enclave to support collaborative and interactive
analysis of sensitive data. Our model uses Jupyter notebooks as a flexible
analysis environment and Python language constructs to support the execution of
arbitrary functions on private data within this secure framework.Comment: To appear in Proceedings of Workshop on Scientific Cloud Computing,
Washington, DC USA, June 2017 (ScienceCloud 2017), 7 page
A perspective on the Healthgrid initiative
This paper presents a perspective on the Healthgrid initiative which involves
European projects deploying pioneering applications of grid technology in the
health sector. In the last couple of years, several grid projects have been
funded on health related issues at national and European levels. A crucial
issue is to maximize their cross fertilization in the context of an environment
where data of medical interest can be stored and made easily available to the
different actors in healthcare, physicians, healthcare centres and
administrations, and of course the citizens. The Healthgrid initiative,
represented by the Healthgrid association (http://www.healthgrid.org), was
initiated to bring the necessary long term continuity, to reinforce and promote
awareness of the possibilities and advantages linked to the deployment of GRID
technologies in health. Technologies to address the specific requirements for
medical applications are under development. Results from the DataGrid and other
projects are given as examples of early applications.Comment: 6 pages, 1 figure. Accepted by the Second International Workshop on
Biomedical Computations on the Grid, at the 4th IEEE/ACM International
Symposium on Cluster Computing and the Grid (CCGrid 2004). Chicago USA, April
200
Data provenance tracking as the basis for a biomedical virtual research environment
In complex data analyses it is increasingly important to capture information about the usage of data sets in addition to their preservation over time to ensure reproducibility of results, to verify the work of others and to ensure appropriate conditions data have been used for specific analyses. Scientific workflow based studies are beginning to realize the benefit of capturing this provenance of data and the activities used to process, transform and carry out studies on those data. This is especially true in biomedicine where the collection of data through experiment is costly and/or difficult to reproduce and where that data needs to be preserved over time. One way to support the development of workflows and their use in (collaborative) biomedical analyses is through the use of a Virtual Research Environment. The dynamic and distributed nature of Grid/Cloud computing, however, makes the capture and processing of provenance information a major research challenge. Furthermore most workflow provenance management services are designed only for data-flow oriented workflows and researchers are now realising that tracking data or workflows alone or separately is insufficient to support the scientific process. What is required for collaborative research is traceable and reproducible provenance support in a full orchestrated Virtual Research Environment (VRE) that enables researchers to define their studies in terms of the datasets and processes used, to monitor and visualize the outcome of their analyses and to log their results so that others users can call upon that acquired knowledge to support subsequent studies. We have extended the work carried out in the neuGRID and N4U projects in providing a so-called Virtual Laboratory to provide the foundation for a generic VRE in which sets of biomedical data (images, laboratory test results, patient records, epidemiological analyses etc.) and the workflows (pipelines) used to process those data, together with their provenance data and results sets are captured in the CRISTAL software. This paper outlines the functionality provided for a VRE by the Open Source CRISTAL software and examines how that can provide the foundations for a practice-based knowledge base for biomedicine and, potentially, for a wider research community
Research Objects: Towards Exchange and Reuse of Digital Knowledge
What will researchers be publishing in the future? Whilst there is little question that the Web will be the publication platform, as scholars move away from paper towards digital content, there is a need for mechanisms that support the production of self-contained units of knowledge and facilitate the publication, sharing and reuse of such entities.

 In this paper we discuss the notion of _research objects_, semantically rich aggregations of resources, that can possess some scientific intent or support some research objective. We present a number of principles that we expect such objects and their associated services to follow
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
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