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DKUM
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Title:
Sistem za samodejno profiliranje zmogljivosti spletnih strani : magistrsko delo
Authors:
ID
Vidovič, Blaž
(Author)
ID
Lukač, Niko
(Mentor)
More about this mentor...
ID
Bizjak, Marko
(Comentor)
Files:
MAG_Vidovic_Blaz_2023.pdf
(2,96 MB)
MD5: 9CD02C489F42C0D027462BECD8ADB35E
Language:
Slovenian
Work type:
Master's thesis/paper
Typology:
2.09 - Master's Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V magistrskem delu raziščemo področje profiliranja zmogljivosti spletnih strani. Seznanimo se z dejavniki, ki vplivajo na zmogljivost spletnih strani in metrikami, s katerimi se meri njihova zmogljivost. Na osnovi danih metrik razvijemo spletno platformo za samodejno profiliranje zmogljivosti spletnih strani. V rezultatih predstavimo uporabo spletne platforme za profiliranje zmogljivosti čelnih in zalednih delov raznolikih spletnih strani.
Keywords:
profiliranje
,
zmogljivost
,
spletna stran
,
uporabniška izkušnja
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[B. Vidovič]
Year of publishing:
2023
Number of pages:
1 spletni vir (1 datoteka PDF (VIII, 74 f.))
PID:
20.500.12556/DKUM-84705
UDC:
004.774.6(043.2)
COBISS.SI-ID:
168591107
Publication date in DKUM:
17.08.2023
Views:
448
Downloads:
103
Metadata:
Categories:
KTFMB - FERI
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:
VIDOVIČ, Blaž, 2023,
Sistem za samodejno profiliranje zmogljivosti spletnih strani : magistrsko delo
[online]. Master’s thesis. Maribor : B. Vidovič. [Accessed 14 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=84705
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Licences
License:
CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:
The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:
12.07.2023
Secondary language
Language:
English
Title:
System for automatic performance profiling of websites
Abstract:
In this master's thesis we explore the field of website performance profiling. We familiarize ourselves with the factors that influence website’s performance and the metrics used to measure the performance. Based on these metrics, we develop a web platform for automatic websites’ performance profiling. In the results, we present the use of the web platform for profiling the performance of front-end and back-end parts of various websites.
Keywords:
profiling
,
performance
,
website
,
user experience
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