Scope

The availability of data has changed dramatically over the past ten years. The wide distribution of web-enabled mobile devices and the evolution of web 2.0 technologies are contributing to a large amount of data (so-called Big Data) [1]. Usable access to complex and large amounts of data poses, e.g., an immense challenge for current solutions in, e.g., business analytics. Handling the complexity of relevant data (generated through information deluge and being targeted with Big Data technologies) requires new techniques about data access, visualization, perception, and interaction for innovative and successful strategies. As a consequence research communities as well as industry, but especially research teams at small universities and Small and Medium-sized Enterprises (SMEs) will be faced with enormous challenges. Furthermore, current e-Science research resources and infrastructures (i.e., data, tools, and related Communication Technology (ICT) services) are often confined to computer science expert usage only and fail to leverage the abundant opportunities that distributed, dynamic, and eventually interdisciplinary Virtual Research Environments (VREs) can provide to scientists, industrial research users as well as to learners in computer science, data science and related educational environments.

The scientific resources like other infrastructures is influenced by globalization. Scientists have been both motivated and enabled to work across disciplinary and international borders by technological advances beyond geographical or institutional boundaries [2]. This trend calls for innovative, dynamic and ubiquitous research supporting VREs where scattered scientists can seamlessly access data, software, and processing resources managed by diverse systems in separate administration domains through their web browser [3]. Therefore, nowadays a VRE allows multiple researchers in different locations to work together in real time without restrictions as it was, e.g., already very early described by UK’s Joint Information Systems Committee (JISC) VRE Collaborative Landscape Study in 2010 [4] as “a platform to help researchers from all disciplines to work collaboratively by managing the increasingly complex range of tasks involved in carrying out research on both small and large scales” [6, 4].

However, these VREs lack cognitive efficient and effective Human-Computer Interaction (HCI) support and overall interoperability in existing approaches. In detail this means, VREs lack standardized and user-centered access to as well as cognitive efficient configuration, management, and usage of research resources during the execution of collaborative research processes where intermediate results have to be shared between interdisciplinary teams and their organizations in scientific communities or industry. Furthermore, they lack a generic and user-centered service infrastructure supporting the entire life cycle of VREs, i.e., allowing for cognitive efficient interactive/visually direct manipulative set-up, configuration, integration, management, and usage of existing research resources and their easy on-demand deployment (e.g., big data stacks for dealing with aspects of data variety, volume and velocity) that harnesses existing large scale research infrastructures, such as as, e.g., the EGI Federated Cloud [5]. Finally, they lack instructional support on how to create, configure, deploy, manage and collaborate in VREs. In order to address human-computer interaction, cognitive efficiency, and interoperability problems, a generic information visualization, user empowerment, as well as service integration and mediation approach based on the existing state-of-the-art in the relevant areas of computer science as well as established open ICT industry standards has to be achieved. This proposal will address these issues with a special focus on supporting distributed Big Data Analysis in VREs.

The purpose of this research road-mapping workshop is threefold. Firstly, it aims to consolidate information, technical details, and research directions from the diverse range of academic and industrial R&D projects currently available. Secondly, based on visions of future infrastructures, gaps in the current state of the art will be determinated and thirdly, new areas of research, which require fund and support along with new areas for collaboration outside, will be identified..

We articulate a clear research, education and training agenda that tracks and builds upon existing and emerging technologies and that respond to future visions of Advanced Visual Interface infrastructures supporting Big Data Applications in Virtual Research Environments. The process will develop a baseline by drawing upon all of our previous workshops and other activities within our recent research projects and will produce a research, technology and development road map. During the workshop we will map out future visions and complete a gap analysis. By treating this over the course of the day we can feedback to refine the baseline (to be captured into a workshop report and corresponding publications) and real research, technology and development gaps, which should be detailed in the road map. For this workshop we are adopting idealization, knowledge capture and road mapping approaches and activities developed during the EU FP7 projects INNOVANET, APARSEN, SCIDIP-ES, and the H2020 projects EDISON, SENSECARE and METAPLAT.

The overall scope and goal of the workshop is to achieve a road map, which can support the acceleration in research, education and training activities by means of transforming, enriching, and deploying advanced visual user interfaces for managing and using eScience infrastructures in VREs. In this way, the research, education and training road map will pave the way towards establishing an infrastructure for a visual user interface tool suite supporting VRE platforms that can host Big Data analysis and corresponding research activities sharing distributed research resources (i.e., data, tools, and services) by adopting common existing open standards for access, analysis and visualization. Thereby, this research helps realizing a ubiquitous collaborative workspace for researchers which is able to facilitate the research process and its Big Data Analysis applications.

Researchers are welcome that work on different scopes of these problem spaces.

References:

[1] J. Freiknecht. Big Data in der Praxis. Carl Hanser Verlag GmbH & Co. KG, M√ľnchen, Deutschland, 2014.

[2] J. Wilsdon et al. Knowledge, networks and nations: Global scientific collaboration in the 21st century. The Royal Society, 2011.

[3] L. Candela. Virtual research environments. Technical report, Networked Multimedia Information System Laboratory, Italian National Research Council, 2011.

[4] A. Carusi and T. Reimer. Virtual research environment collaborative landscape study. JISC, Bristol, page 106, 2010.

[5] EGI Foundation – EGI.eu. Egi federated cloud. https://www.egi.eu/infrastructure/cloud/. Accessed: 2016-01-10.

[6] Helmholtz-Gemeinschaft. Definition: Virtual research environments. http://www.allianzinitiative.de/en/core activities/virtual research environments/definition/, Feb 2011. Accessed: 2016-01-11.


Acknowledgements and Disclaimer

This workshop proposal has been produced in the context of the EDISON project. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 675419. However, this workshop proposal reflects only the author’s view and the European Commission is not responsible for any use that may be made of the information it contains.