E-finance-lab at the House of Finance : about us

The financial services industry is believed to be on the verge of a dramatic [r]evolution. A substantial redesign of its value chains aimed at reducing costs, providing more efficient and flexible services and enabling new products and revenue streams is imminent. But there seems to be no clear migration path nor goal which can cast light on the question where the finance industry and its various players will be and should be in a decade from now. The mission of the E-Finance Lab is the development and application of research methodologies in the financial industry that promote and assess how business strategies and structures are shared and supported by strategies and structures of information systems. Important challenges include the design of smart production infrastructures, the development and evaluation of advantageous sourcing strategies and smart selling concepts to enable new revenue streams for financial service providers in the future. Overall, our goal is to contribute methods and views to the realignment of the E-Finance value chain. ..

From Bare Metal to Virtual: Lessons Learned when a Supercomputing Institute Deploys its First Cloud

As primary provider for research computing services at the University of Minnesota, the Minnesota Supercomputing Institute (MSI) has long been responsible for serving the needs of a user-base numbering in the thousands. In recent years, MSI---like many other HPC centers---has observed a growing need for self-service, on-demand, data-intensive research, as well as the emergence of many new controlled-access datasets for research purposes. In light of this, MSI constructed a new on-premise cloud service, named Stratus, which is architected from the ground up to easily satisfy data-use agreements and fill four gaps left by traditional HPC. The resulting OpenStack cloud, constructed from HPC-specific compute nodes and backed by Ceph storage, is designed to fully comply with controls set forth by the NIH Genomic Data Sharing Policy. Herein, we present twelve lessons learned during the ambitious sprint to take Stratus from inception and into production in less than 18 months. Important, and often overlooked, components of this timeline included the development of new leadership roles, staff and user training, and user support documentation. Along the way, the lessons learned extended well beyond the technical challenges often associated with acquiring, configuring, and maintaining large-scale systems.Comment: 8 pages, 5 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

Complete LibTech 2013 Print Program

PDF of the complete print program from the 2013 Library Technology Conferenc

Simplifying the Development, Use and Sustainability of HPC Software

Developing software to undertake complex, compute-intensive scientific processes requires a challenging combination of both specialist domain knowledge and software development skills to convert this knowledge into efficient code. As computational platforms become increasingly heterogeneous and newer types of platform such as Infrastructure-as-a-Service (IaaS) cloud computing become more widely accepted for HPC computations, scientists require more support from computer scientists and resource providers to develop efficient code and make optimal use of the resources available to them. As part of the libhpc stage 1 and 2 projects we are developing a framework to provide a richer means of job specification and efficient execution of complex scientific software on heterogeneous infrastructure. The use of such frameworks has implications for the sustainability of scientific software. In this paper we set out our developing understanding of these challenges based on work carried out in the libhpc project.Comment: 4 page position paper, submission to WSSSPE13 worksho

Harnessing data flow and modelling potentials for sustainable development

Tackling some of the global challenges relating to health, poverty, business and the environment is known to be heavily dependent on the flow and utilisation of data. However, while enhancements in data generation, storage, modelling, dissemination and the related integration of global economies and societies are fast transforming the way we live and interact, the resulting dynamic, globalised and information society remains digitally divided. On the African continent, in particular, the division has resulted into a gap between knowledge generation and its transformation into tangible products and services which Kirsop and Chan (2005) attribute to a broken information flow. This paper proposes some fundamental approaches for a sustainable transformation of data into knowledge for the purpose of improving the peoples' quality of life. Its main strategy is based on a generic data sharing model providing access to data utilising and generating entities in a multi disciplinary environment. It highlights the great potentials in using unsupervised and supervised modelling in tackling the typically predictive-in-nature challenges we face. Using both simulated and real data, the paper demonstrates how some of the key parameters may be generated and embedded in models to enhance their predictive power and reliability. Its main outcomes include a proposed implementation framework setting the scene for the creation of decision support systems capable of addressing the key issues in society. It is expected that a sustainable data flow will forge synergies between the private sector, academic and research institutions within and between countries. It is also expected that the paper's findings will help in the design and development of knowledge extraction from data in the wake of cloud computing and, hence, contribute towards the improvement in the peoples' overall quality of life. To void running high implementation costs, selected open source tools are recommended for developing and sustaining the system. Key words: Cloud Computing, Data Mining, Digital Divide, Globalisation, Grid Computing, Information Society, KTP, Predictive Modelling and STI