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Cited by 19Data Mining and Machine Learning ApplicationsData Mining Algorithms and ApplicationsLogic, programming, and type systems

Performance Analysis of Selected Programming Languages in the Context of Supporting Decision-Making Processes for Industry 4.0

Open access
Dymora, Paweł, Paszkiewicz, Andrzej·2020·Applied Sciences
DOI 10.3390/app10238521
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Abstract

This study analyzes the possibility of using Go (Golang) in the context of Java and Python in decision-making processes, with particular emphasis on their use in industry-specific solutions for Industry 4.0. The authors intentionally compared Go with Java and Python, which have been widely used for many years for data analysis in many areas. The research work was based on decision trees data mining algorithms, and especially on classification trees, in which the measure of entropy as a heuristics to choose an attribute was taken into account. The tests were carried out on various parameters describing calculation time, RAM usage, and CPU usage. The source data, which were the basis for the computing of the decision tree algorithm implemented using these three languages, were obtained from a commercial remote prototyping system and were related to the target customers’ choice of methods and means of the full design-creation process.

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  • A study by Dymora et al. found that for processing small amounts of data, Go surpassed comparable languages in performance, CPU and memory utilisation.

    “A study by Dymora et al. remarked that for processing small amounts of data, Go surpassed comparable languages in terms of performance, CPU and memory utilisation [24]”

    From A Distributed Edge FLISR Solution & Network Simulation Test Platform (2024) · cites this paper as [24]

    Table 3 (Conclusions; page unavailable) shows that for the smallest dataset, Go used less CPU and RAM than Java and Python, and ran faster than Python. But Java had a shorter computation time than Go, so the claim that Go surpassed both comparable languages in performance is too broad. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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1 of the 76 references couldn’t be matched to a known paper.

Research works printed but not linked: 7

  1. Voellmy, A.; Kim, H.; Feamster, N. Procera: A language for high-level reactive network control. In Proceedings of the 1st Workshop Hot Topics Software Defined Networks, Helsinki, Finland, 13 August 2012; Association for Computing Machinery: New York, NY, USA, 2012; pp. 43-48.
  2. Dymora, P.; Mazurek, M.; Łannik, D. Badanie efektywności tworzenia wielowymiarowych zestawów danych w wybranych środowiskach analitycznych, Monografia pt. In Social and Technical Aspects of Security; Oficyna Wydawnicza Politechniki Rzeszowskiej: Rzeszów, Poland, 2019; pp. 37-57, ISBN 978-83-7934-352-2.
  3. Wilson, L.B.; Clark, R.G. Comparative Programming Languages, 3rd ed.; Addison-Wesley: Harlow, UK, 2001.
  4. Sebesta, R.W. Concepts of Programming Languages, 10th ed.; Pearson Education, Inc.: Upper Saddle River, NJ, USA, 2012.
  5. Reddy, K.P.N.; Geyavalli, Y.; Sujani, D.; Rajesh, S.M. Comparison of Programming Languages: Review. Int. J. Comput. Sci. Commun. 2018, 9, 113-122.
  6. Olson, D.L.; Delen, D. Advanced Data Mining Techniques; Springer: Berlin, Germany, 2008.
  7. Tan, P.-N.; Steinbach, M.; Karpatne, A.; Kumar, V. Introduction to Data Mining, 2nd ed.; Pearson: London, UK, 2018.

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Authors
Dymora, P., & Paszkiewicz, A.
Affiliations
Rzeszów University of Technology (PL)
Corresponding author
Dymora, Paweł
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2020-11-28
Volume
10
Issue
23
Pages
8521
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app10238521
OpenAlex
W3108349105
Cited by
18
References
76
Citations by year
2026: 2 · 2024: 3 · 2023: 10 · 2022: 2 · 2021: 1
FWCI
3.06
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Ministerstwo Nauki i Szkolnictwa Wyższego (027/RID/2018/19)
Topics
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Logic, programming, and type systems
Keywords
programming language performance, Java, Python, decision tree, classification trees, Industry 4.0, decision support, computational time, memory usage, CPU utilization

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