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نتیجه جستجو - مطالعه نگاشت سیستمی

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Programming languages for data-Intensive HPC applications: A systematic mapping study
زبان های برنامه نویسی برای برنامه های HPC با داده های فشرده: یک مطالعه نگاشت منظم-2020
A major challenge in modelling and simulation is the need to combine expertise in both software tech- nologies 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 character- istics 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 en- abled 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 ex- perts. 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.
Keywords: High performance computing (HPC) | Big data | Data-intensive applications | Programming languages | Domain-Specific language (DSL) | General-Purpose language (GPL) | Systematic mapping study (SMS)
مقاله انگلیسی
2 Toward a taxonomy of entrepreneurship education research literature: A bibliometric mapping and visualization
به سمت طبقه بندی ادبیات تحقیق در زمینه آموزش کارآفرینی: یک نقشه برداری و تجسم کتابشناختی-2019
The retrospective amount of research literature dedicated to entrepreneurship education (EE) is overwhelming, which makes producing an overview difficult. However, advanced bibliometric mapping and clustering techniques can help visualize and structure complex research literature. Thus, the objective of this mapping study is to systematically explore and cluster the EE research literature to deliver a taxonomic scheme that can serve as a basis for future research. The analyzed data, which were drawn from the Web of Science and Scopus, consist of 1773 peer-reviewed documents published between 1975 and 2014. On the one hand, this taxonomy should create stronger ties to educational research; on the other, it can foster international research collaboration to boost both interdisciplinary EE and its impact on a global basis. This work reinforces our understanding of current EE research by identifying and distilling the most powerful intellectual relationships among its contributions and contributors. Consequently, this study addresses not only the academic community but also entrepreneurship educators and policymakers in an effort to boost entrepreneurial spirit, design effective policy instruments, and, ultimately, improve societal welfare.
Keywords: Entrepreneurship education | Bibliometric mapping | Bibliometric visualization | Systematic mapping study | TaxonomyJEL classification:L26 | Entrepreneurship
مقاله انگلیسی
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