Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Methodologies for designing healthcare analytics solutions: a literature analysis

© The Author(s) 2019. Healthcare analytics has been a rapidly emerging research domain in recent years. In general, healthcare solution design studies focus on developing analytic solutions that enhance product, process and practice values for clinical and non-clinical decision support. The objective of this study is to explore the scope of healthcare analytics research and in particular its utilisation of design and development methodologies. Using six prominent electronic databases, qualifying articles between 2010 and mid-2018 were sourced and categorised. A total of 52 articles on healthcare analytics solutions were selected for relevant content on public healthcare. The research team scrutinised the articles, using established content analysis protocols. Analysis identified that various methodologies have been used for developing analytics solutions, such as prototyping, traditional software engineering, agile approaches and others, but despite its clear advantages, few show the use of design science. Key topic areas are also identified throughout the content analysis suggesting topical research priorities in the field

A model for Business Intelligence Systems’ Development

Often, Business Intelligence Systems (BIS) require historical data or data collected from var-ious sources. The solution is found in data warehouses, which are the main technology used to extract, transform, load and store data in the organizational Business Intelligence projects. The development cycle of a data warehouse involves lots of resources, time, high costs and above all, it is built only for some specific tasks. In this paper, we’ll present some of the aspects of the BI systems’ development such as: architecture, lifecycle, modeling techniques and finally, some evaluation criteria for the system’s performance.BIS (Business Intelligence Systems), Data Warehouses, OLAP (On-Line Analytical Processing), Object-Oriented Modeling

Information Sharing for Customized Dynamic Visual Analytics: A Framework

Supply chain activities generate massive amount of data by several actors such as, suppliers, manufacturers, warehouses, distributers, and wholesalers. Visual analytics (VA) plays a key role in knowledge discovery and insight generation from this data and helps various players to enhance their operational and strategic decision making. This is more essential for Fast moving consumer goods (FMCG) industry, given the size of the industry and its sensitivity to the diverse market uncertainties. In this paper, we present a PhD research plan that responds to the requirements of a FMCG supply chain VA system by means of a comprehensive framework. In this regard, the information flow throughout the supply chain is a significant factor for developing a reliable and efficient VA solution and a proper information flow throughout the supply chain can be enhanced with the help of the framework consisting of modules including Data Generation, Data Integration and Management, Data Analytics, Data Visualization, and Data-driven decision making. The aim of the study is to explore the development of a VA framework that acts as a guideline for supply chain players to improve their analytical capabilities.publishedVersio

CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

Capability-actor-resource-service : a conceptual modelling approach for value-driven strategic sourcing

This PhD research addresses a problem within strategic sourcing, which is a critical area of strategic management that is centered on decision-making related to procurement. Strategic sourcing is related to two disciplines: (i) procurement and supply management and (ii) strategic management. Sourcing is the strategic part of procurement that refers to tasks like determining cost saving and value-driven opportunities, choosing the most appropriate go-to market strategies, and selecting and evaluating suppliers for building long-term and short-term contractual relationships. Many companies face challenges in obtaining the benefits associated with effective strategic sourcing. Although the concept of strategic sourcing is fairly well recognized, managers are still challenged by many barriers to its implementation. The main problem is the lack of practical instruments (i.e., tools and techniques) to implement the value-driven management approach to strategic sourcing, while at the same time preparing companies for fact-based decision-making by delivering data management and data analytics capabilities. This is the problem which is addressed with this PhD research. To address this problem, the research goal has been defined as “develop a modeling approach that enables companies 1) to drive fact-based decision-making with respect to procurement data management and procurement analytics”; and 2) to implement strategic sourcing toward achieving value-driven targets”. We apply conceptual modeling as our main solution approach to achieve the above research goal. We define three major areas where conceptual modeling can contribute to strategic sourcing decision-making: conceptualization, design and computer support. The proposed conceptual modeling approach is characterized by four different perspectives: (i) a way of thinking (i.e., a conceptual foundation), (ii) a way of modeling (i.e., a modeling language and method to use it), (iii) a way of working (i.e., a model-based analysis approach), and (iv) a way of supporting (i.e., a computer-aided design tool). The scope of PhD research is limited to the first three perspectives, while for the fourth perspective a solution architecture will be proposed as part of future research. This PhD dissertation is a paper-based dissertation consisting of six chapters. Three chapters (chapter 3, 4, 5) of this dissertation have been submitted to international peer-reviewed journals (chapter 4 is published and chapters 3 and 5 are accepted) and one chapter (chapter 2) has been published in the post-conference proceedings of an international workshop