Wyniki wyszukiwania - BazTech

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Industry 4.0: selected aspects of algorithmization of work environment

Osika Grażyna

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2022

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z. 155

431--450

EN

Purpose: The aim of the article is to describe and forecast possible difficulties related to the development of cognitive technologies and the progressing of algorithmization of HRM processes as a part of Industry 4.0. Design/methodology/approach: While most of the studies to date related to the phenomenon of Industry 4.0 and Big Data are concerned with the level of efficiency of cyber-physical systems and the improvement of algorithmic tools, this study proposes a different perspective. It is an attempt to foresee the possible difficulties connected with algorithmization HRM processes, which understanding could help to "prepare" or even eliminate the harmful effects we may face which will affect decisions made in the field of the managing organizations, especially regarding human resources management, in era of Industry 4.0. Findings: The research of cognitive technologies in the broadest sense is primarily associated with a focus of thinking on their effectiveness, which can result in a one-sided view and ultimately a lack of objective assessment of that effectiveness. Therefore, conducting a parallel critical reflection seems even necessary. This reflection has the potential to lead to a more balanced assessment of what is undoubtedly "for", but also of what may be "against". The proposed point of view may contribute to a more informed use of algorithm-based cognitive technologies in the human resource management process, and thus to improve their real-world effectiveness. Social implications: The article can have an educational function, helps to develop critical thinking about cognitive technologies, and directs attention to areas of knowledge by which future skills should be extended. Originality/value: This article is addressed to all those who use algorithms and data-driven decision-making processes in HRM. Crucial in these considerations is the to draw attention to the dangers of unreflective use of technical solutions supporting HRM processes. The novelty of the proposed approach is the identification of three potential risk areas that may result in faulty HR decisions. These include the risk of "technological proof of equity", overconfidence in the objective character of algorithms and the existence of a real danger resulting from the so-called algorithm overfitting. Recognition of these difficulties ultimately contributed to real improvements in productivity by combining human performance with technology effectiveness.

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Improving the university recruitment process with web analytics

Wiechetek Łukasz, Mędrek Marek

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2022

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z. 158

679--695

EN

Purpose: The main aim of the article is to know the information needs of candidates for university courses and indicate the importance of web analytics tools in the university recruitment process. The authors present the recruitment process for data science high study programme that was conducted in the middle of 2021 at one of the biggest universities in eastern Poland. Theoretical background: Digital transformation is an irreversible process today. Data produced by people, things, administration units and business organizations can be the source of valuable information. That transformation causes new possibilities for fast development, but also creates challenges for education processes and professional work. Furthermore, the digital transformation resulted in creating new professions like data science (DS). Because of data volume and its importance DS professionals became one of the most wanted specialists in the 21st century, and therefore many universities try to launch new study programs related to automated data processing and try to get the attention of potential students. Design/methodology/approach: The process was supported with analytics tools Hotjar and Google Analytics. The results presented in the paper base on the analysis of 974 pageviews recorded by Hotjar and activity of 824 page users reported by Google Analytics. Findings: The analysis showed that web analytics tools are very easy to use in the recruitment process, and that gathered data allows for better understanding of candidates' needs and improving the future requirement processes and tools. Results indicated that the most important topics for candidates were study programme and payment. Form the technical point of view the responsiveness of applications used for the recruitment process is crucial because a lot of traffic was generated by both users of desktop computers and mobile devices. The greatest interest in the program was recorded before the holiday months. Originality/value: The research contributes to academia in the field of recruitment. Paper presents the data science high study programme and indicates the importance of web analytics tools in the university recruitment process.

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OECD Recommendation’s draft concerning access to research data from public funding: a review

Madeyski Lech, Lewowski Tomasz, Kitchenham Barbara

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 1

art. no. e135401

EN

Sharing research data from public funding is an important topic, especially now, during times of global emergencies like the COVID-19 pandemic, when we need policies that enable rapid sharing of research data. Our aim is to discuss and review the revised Draft of the OECD Recommendation Concerning Access to Research Data from Public Funding. The Recommendation is based on ethical scientific practice, but in order to be able to apply it in real settings, we suggest several enhancements to make it more actionable. In particular, constant maintenance of provided software stipulated by the Recommendation is virtually impossible even for commercial software. Other major concerns are insufficient clarity regarding how to finance data repositories in joint private-public investments, inconsistencies between data security and user-friendliness of access, little focus on the reproducibility of submitted data, risks related to the mining of large data sets, and sensitive (particularly personal) data protection. In addition, we identify several risks and threats that need to be considered when designing and developing data platforms to implement the Recommendation (e.g., not only the descriptions of the data formats but also the data collection methods should be available). Furthermore, the non-even level of readiness of some countries for the practical implementation of the proposed Recommendation poses a risk of its delayed or incomplete implementation.

4

Big problems with Big Data

Goczyła Krzysztof

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2020

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Vol. 24, No 1

73--81

EN

The article presents an overview of the most important issues related to the phenomenon called big data. The characteristics of big data concerning the data itself and the data sources are presented. Then, the big data life cycle concept is formulated. The next sections focus on two big data technologies: MapReduce for big data processing and NoSQL databases for big data storage.

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Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit

Janusz Andrzej, Przyborowski Mateusz, Biczyk Piotr, Ślęzak Dominik

Annals of Computer Science and Information Systems

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2020

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Vol. 21

77--80

EN

FedCSIS 2020 Data Mining Challenge: Network Device Workload Prediction was the seventh edition of the international data mining competition organized at Knowledge Pit, in association with the Conference on Computer Science and Information Systems. The main goal was to answer the question of whether it is possible to reliably predict workload-related characteristics of monitored network devices based on historical readings. We describe the scope and explain the motivation for this challenge. We also analyze solutions uploaded by the most successful participants and investigate prediction errors which had the greatest influence on the results. Finally, we describe our baseline solution to the considered problem, which turned out to be the most reliable in the final evaluation.

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Hybrydowy system rekomendacji planów treningowych

Kaczanowski Maciej

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

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2019

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nr 20

29--40

PL

Hybrydowe systemy rekomendacji łączą zalety metod stosowanych powszechnie w rekomendacji. Głównym celem tego artykułu jest przedstawienie zastosowania uczenia maszynowego do budowy hybrydowego silnika rekomendacji. Uczenie maszynowe jest poddziedziną sztucznej inteligencji, która wykazuję obiecujące rezultaty w klasyfikacji, predykcji, wykrywaniu anomalii i rekomendacji. W tym artykule zaproponowano koncepcję spersonalizowanego modelu systemu rekomendacji opartego na parametrach i planach treningowych sportowców. Badania przeprowadzono w środowisku chmurowym Microsoft Azure Machine Learning Studio na zbiorze danych wygenerowanym na podstawie danych referencyjnych.

EN

Hybrid recommendation systems combine the advantages of commonly used methods in recommendations. This main objective of this article is to present application of machine learning to build a hybrid recommendation engine. Machine learning is subdomain of artificial intelligence that show promising results in classification, prediction, anomaly detection and recommendations. This paper proposed a personalized recommendation system model based on athletes parameters and training plans. The researches were carried out in the cloud environment Microsoft Azure Machine Learning Studio on football data set.