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- Amaral, Vasco, et al.
(författare)
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Programming Languages for Data-Intensive HPC Applications : a Systematic Mapping Study
- 2020
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Ingår i: Parallel Computing. - : Elsevier. - 0167-8191 .- 1872-7336. ; 91, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006–2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.
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| 2. |
- Bednar, Peter, et al.
(författare)
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Supporting Business Decision-making: One Professional at a Time
- 2014
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Ingår i: DSS 2.0 – Supporting Decision Making with New Technologies. - 9781614993988 - 9781614993995 ; 261, s. 471-482
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Bokkapitel (refereegranskat)abstract
- This paper discusses the potential for personalized, user-owned decision-support systems. It can be readily seen that there are benefits from analysis of ‘Big Data’ that could not be attained through more traditional means, e.g. insurance and credit card fraud can be detected more readily when it is possible to analyze integrated data across multiple servers owned and controlled by separate organizations. However, high-level data analysis, though useful, cannot be trusted to provide all the answers to organizational ‘questions’. Individuals need to be able to inform themselves in complex decision situations and for this purpose there can be no substitute for ‘little data’ from wherever this is to be drawn. We explore a potential type of support that could overcome the barriers to professional creativity arising through lack of trust in decision-support systems owned and controlled from senior management. The Virtual Personal Assistant described uses natural language processing to interact with a professional user in the context of messy, situated problems, and in private. It has capability to learn from user-interactions and therefore to co-evolve contextually. A ‘little data’ system such as this can therefore help to improve relevance of user understandings in a relatively risk free environment.
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| 4. |
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| 5. |
- Carlsson, Sven, et al.
(författare)
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BI Views: Connection, Immersion, and Fusion Through the Lens of Emergence
- 2012
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Ingår i: Fusing decision support systems into the fabric of the context. - 9781614990727 ; , s. 125-136
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Bokkapitel (populärvet., debatt m.m.)abstract
- In the debate on the core of Information System (IS), El Sawy identified three faces of IS views: connection, immersion, and fusion. However, no research has further elaborated these concepts. For this task, we adopted the characteristics of the old concept of “emergence” to better describe and explain the shifts in IS from connection to immersion and finally towards fusion. A police organization is examined as a case study, and its Business Intelligence (BI) system and users and their interaction in solving daily tasks are observed. The study took place during two different time periods in 2009 and 2011. Both quantitative and qualitative methods were used, including a survey and interviews. Two main user groups were identified in the police organization: viewers and analysts. The results of this study showed that in the context of viewers BI is in connection view and is slowly emerging into immersion. On the other hand, in the context of analysts BI is positioned in immersion view and, based on emergent properties, we concluded that there is potential for a slow emergence towards fusion once more capabilities are integrated in the BI system used by the analysts.
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| 6. |
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DSS 2.0 – Supporting Decision Making With New Technologies
- 2014
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Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- Advances in technology have resulted in new and advanced methods to support decision-making. For example, artificial intelligence has enabled people to make better decisions through the use of Intelligent Decision Support Systems (DSS). Emerging research in DSS demonstrates that decision makers can operate in a more timely manner using real-time data, more accurately due to data mining and 'big data' methods, more strategically by considering a greater number of factors, more precisely and inclusively due to the availability of social networking data, and with a wider media reach with video and audio technology. This book presents the proceedings of the IFIP TC8/Working Group 8. 3 conference held at the UniversitU Pierre et Marie Curie in Paris, France, in June 2014. Throughout its history the conference has aimed to present the latest innovations and achievements in Decision Support Systems. This year the conference looks to the next generation with the theme of new technologies to enable DSS2. 0. The topics covered include theoretical, empirical and design science research; case-based approaches in decision support systems; decision models in the real-world; healthcare information technology; decision making theory; knowledge management; knowledge and resource discovery; business intelligence; group decision support systems; collaborative decision making; analytics and aebig dataAE; rich language for decision support; multimedia tools for DSS; Web 2. 0 systems in decision support; context-based technologies for decision making; intelligent systems and technologies in decision support; organizational decision support; research methods in DSS 2. 0; mobile DSS; competing on analytics; and social media analytics. The book will be of interest to all those who develop or use Decision Support Systems. The variety of methods and applications illustrated by this international group of carefully reviewed papers should provide ideas and directions for future researchers and practitioners alike.
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| 7. |
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| 8. |
- Tona, Olgerta, et al.
(författare)
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The Impact of the Organizing Vision on Mobile BI Adoption
- 2014
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Ingår i: DSS 2.0 – Supporting Decision Making with New Technologies. - 0922-6389. - 9781614993988 ; 261, s. 303-313
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Konferensbidrag (refereegranskat)abstract
- Mobile Business Intelligence (m-BI) enhances the capabilities of organizations to take decisions 'on-the-move'. M-BI emerged as an extension of BI and many companies have or are considering implementing it. Being an innovation, m-BI reflects an organizing vision, created by a broad community, which is prominent during the comprehension process of this innovation. Little is known how the organizing vision affects the decision to adopt a certain innovation, and moreover these studies are completely lacking within the m-BI area. A qualitative approach has been embraced and interviews with different organizations that have already decided to adopt m-BI have been conducted. This study revealed that industrial research reports, vendors marketing campaigns and success stories produced by the collaboration among vendors, analysts, journalist and early adopters have a strong influence on the early phases of the organizational decision-making process to adopt m-BI. Increasing internal and external legitimacy, reducing the risk to be left behind, gaining competitive advantage, following the footsteps of high-performing companies, increasing their prestige and reputation, are the main reasons for why organizations may start the decision-making process to adopt m-BI; all this under the umbrella of the organizing vision.
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| 9. |
- Vitabile, Salvatore, et al.
(författare)
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Medical Data Processing and Analysis for Remote Health and Activities Monitoring
- 2019
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Ingår i: High-Performance Modelling and Simulation for Big Data Applications. - Cham : Springer. - 9783030162719 - 9783030162726 ; , s. 186-220
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Bokkapitel (refereegranskat)abstract
- Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human’s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions.
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