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The current article presents the results of a case study dealing with the historical roots of Eastern European researchers in computer science.

The study is based on an analysis of cited references stemming from a collection of around 80,000 computer science papers by Eastern European researchers published from 1989 to 2014. By using a method called “reference publication year spectroscopy” (RPYS) for historical analyses based on bibliometric data, we analyze around 800,000 references cited in those papers. The study identifies the peak years, including most frequently cited publications (from 1952, 1965 and 1975), and focuses on these outstanding works for the field. The research shows how these influential papers were cited in Eastern Europe and in general, and on which scientific fields they have the most impact.

A noteworthy publication that seems to have a tremendous effect on Eastern European computer science is Zadeh's “Fuzzy sets” article which appeared in Information and Control in 1965. The study demonstrates that computer scientists from Eastern Europe are more conservative in their citation behaviour and tend to refer to older and more established research than their counterparts from the West.

Which are the historical roots of researchers working in a particular field or on a specific topic? Are there certain publications – landmark papers – which are important for their research? We guess that these are questions bothering researchers in many fields.

The purpose of this paper is to study the development of research in computer science in 15 Eastern European countries following the breaching of the Berlin Wall in 1989.

The authors conducted a bibliometric analysis of 82,121 computer science publications indexed in the Web of Science database and investigated publication, citation, and collaboration patterns of the individual countries.

Poland has been the most productive country, followed by Russia, the Czech Republic, Romania, Hungary, and Slovenia. Publication rates have increased substantially over the period, but this has not been accompanied by a corresponding increase in the quality of the publications. Hungary and Slovenia are the most influential countries in terms of citations per paper. Artificial Intelligence is the most frequently occurring computer science subject category, with Interdisciplinary Applications the category with the greatest impact. USA, Germany, UK, France, and Canada are the most frequently collaborating western nations, and papers published in collaboration with US authors accrue the most citations.

This is the first ever bibliometric study of the whole post-communist Eastern European computer science research as indexed in the Web of Science.

Project selection management is a matter of challenge for project-oriented organizations, particularly, if the decision-makers are confronted with limited resources. One of the main concerns is selecting an optimal subset that can successfully satisfy the requirements of the organization providing enough resources to the best subset of the project. The projects for which there are not enough resources or those requiring whole resources of the organization will collapse soon after failed to success. Therefore, the issue is in the risk of choosing a set of projects so that can make a balance in investment versus on collective benefit.

A model is presented for project selection and has been tested on the 37 available projects. This model could increase the efficiency of the whole subset of the project significantly in comparison to the other model and it was because of choosing a diverse subset of projects.

Provides a general framework for project selection and a diverse and balanced subset of projects to increase the efficiency of the selected subset. Also, reduces the impact of uncertainty risk on the project selection process.

For the purposes of project selection, any project whose results are uncertain is a risky project because, if the project fails, it will reduce combined project value. For example, a pharmaceutical company’s R&D project is affected by the uncertain results of a specific compound. If the company invests in different compounds, a failure with one will be offset by a good result on another. Therefore, with selecting a diverse set of projects, this paper will have a different set of risks.

This paper discusses the risk of selecting or being responsible for selecting a project under uncertainty. Most of the projects in the field of project selection generally consider the risks facing the projects or existing models that do not take into account the risk.