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Zasnova in implementacija tehnološkega sklada za učinkovito primerjavo velike količine artiklov
ID Janc, Ambrož (Author), ID Jurič, Matjaž Branko (Mentor) More about this mentor... This link opens in a new window

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Abstract
V našem delu smo se ukvarjali z implementacijo primerjalnika cen. Predstavili smo tehnološki sklad za implementacijo primerjalnika cen z uporabo oblačne tehnologije in pripravili prototip. Za implementacijo prototipa smo izbrali ponudnika oblačnih storitev Azure, ki je bil najbolj primeren glede na našo analizo. Prototip smo ocenili z več različnimi testi in določili ali je primeren za ta primer uporabe. Za implementacijo primerjalnika cen smo pripravili dva modela strojnega učenja za primerjavo artiklov, enega z uporabo pristopov analize besedila in drugega z uporabo pristopov slikovne analize. Modela smo med seboj primerjali, ocenili in predstavili boljšega za ta primer uporabe. V delu smo ugotovili, da je model z uporabo analize besedila primernejši model za ta primer uporabe, saj je zmožen natančneje primerjati večje število artilov, kot pa model slikovne analize.

Language:Slovenian
Keywords:Oblačna arhitektura, analiza slik, analiza besedila, Kubernetes, Azure, pridobivanje slik, mikrostoritve, Docker, dogodkovni tok, oblačna arhitektura aplikacij
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FRI - Faculty of Computer and Information Science
Year:2023
PID:20.500.12556/RUL-150595 This link opens in a new window
COBISS.SI-ID:170122755 This link opens in a new window
Publication date in RUL:21.09.2023
Views:501
Downloads:58
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Secondary language

Language:English
Title:Design and implementation of a technological stack for efficient comparison of large amount of items
Abstract:
In our work we implemented a price comparison application. We present a technological stack for the implementation of the application using cloud technologies and present a prototype of the implementation. We used Azure cloud provider for the implementation of the prototype, as it was most appropriate according to our analysis. We evaluated the prototype based on multiple tests and decide, if it is right for this use case. We also presented two machine learning models for comparing products, one model uses text analysis approaches and the other uses image analysis. We compared the models, evaluated their performance and present the better model for our use case. In our work we found that the text analysis model performs better for our use case, since it is able to compare products faster and more accurately.

Keywords:Cloud architecture, image analysis, text analysis, Kubernetes, Azure, image retrieval, microservices, Docker, event stream, cloud-native application architecture

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