Scientific Big Data Analytics by HPC

Storing, managing, sharing, curating and especially analysing huge amounts of data face an immense visibility and importance in industry and economy as well as in science and research. Industry and economy exploit “Big Data” for predictive analysis, to increase the efficiency of infrastructures, customer segmentation, and tailored services. In science, Big Data allows for addressing problems with complexities that were impossible to deal with so far. The amounts of data are growing exponentially in many areas and are becoming a drastical challenge for infrastructures, software systems, analysis methods, and support structures, as well as for funding agencies and legislation.In this contribution, we argue that the Helmholtz Association, with its objective to build and operate large-scale experiments, facilities, and research infrastructures, has a key role in tackling the pressing Scientific Big Data Analytics challenge. DataLabs and SimLabs, sustained on a long-term basis in Helmholtz, can bring research groups together on a synergistic level and can transcend the boundaries between different communities. This allows to translate methods and tools between different domains as well as from fundamental research to applications and industry. We present an SBDA framework concept touching its infrastructure building blocks, the targeted user groups and expected benefits, also concerning industry aspects. Finally, we give a preliminary account on the call for “Expressions of Interest” by the John von Neumann-Institute for Computing concerning Scientific Big Data Analytics by HPC

Polchinski equation, reparameterization invariance and the derivative expansion

The connection between the anomalous dimension and some invariance properties of the fixed point actions within exact RG is explored. As an application, Polchinski equation at next-to-leading order in the derivative expansion is studied. For the Wilson fixed point of the one-component scalar theory in three dimensions we obtain the critical exponents \eta=0.042, \nu=0.622 and \omega=0.754.Comment: 28 pages, LaTeX with psfig, 12 encapsulated PostScript figures. A number wrongly quoted in the abstract correcte

Research Data Alliance: Understanding Big Data Analytics Applications in Earth Science

The Research Data Alliance (RDA) enables data to be shared across barriers through focused working groups and interest groups, formed of experts from around the world - from academia, industry and government. Its Big Data Analytics (BDA) interest groups seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. BDA seeks to analyze different scientific domain applications (e.g. earth science use cases) and their potential use of various big data analytics techniques. These techniques reach from hardware deployment models up to various different algorithms (e.g. machine learning algorithms such as support vector machines for classification). A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. This contribution will outline initial parts of such a classification and recommendations in the specific context of the field of Earth Sciences. Given lessons learned and experiences are based on a survey of use cases and also providing insights in a few use cases in detail

EUDAT – Towards A Pan-European Collaborative Data Infrastructure

The constantly growing amounts of global, diverse, complex, but extremely valuable scientific data is an opportunity, but also a major challenge for research. In recent years, several pan-European e-Infrastructures and a wide variety of research infrastructures have been established supporting multiple research communities. But the accelerated proliferation of data arising from powerful new scientific instruments, scientific simulations and digitization of library resources, for example, have created a more urgent demand for increasing efforts and investments in order to tackle the specific challenges of data management and to ensure a coherent approach to research data access and preservation. A vision of a ‘collaborative data infrastructure’ for science was outlined by the European high level expert group on scientific data listing 12 high level requirements and 24 challenges to overcome. In this talk, we take stock of activities of the pan-European EUDAT collaborative data infrastructure that aims to address these challenges and exploit new opportunities to satisfy many of the high level requirements with concrete data services. Data Analytics techniques in context will be highlighted (e.g. machine learning algorithms, statistical data mining approaches, etc.) in order to advance in science and engineering in ways not possible before