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master's thesis
Otkrivanje anomalija u velikim podacima
2022. urn:nbn:hr:168:248804
Šeketa, Domagoj
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Šeketa, D. (2022). Otkrivanje anomalija u velikim podacima (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:248804

Šeketa, Domagoj. "Otkrivanje anomalija u velikim podacima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:248804

Šeketa, Domagoj. "Otkrivanje anomalija u velikim podacima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:248804

Šeketa, D. (2022). 'Otkrivanje anomalija u velikim podacima', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 April 2024, https://urn.nsk.hr/urn:nbn:hr:168:248804

Šeketa D. Otkrivanje anomalija u velikim podacima [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 April 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:248804

D. Šeketa, "Otkrivanje anomalija u velikim podacima", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:248804

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Metadata
TitleOtkrivanje anomalija u velikim podacima
Title (english)Anomaly Detection in Big Data
AuthorDomagoj Šeketa
MentorKrešimir Pripužić (mentor)
Committee memberKrešimir Pripužić (predsjednik povjerenstva)
Committee memberDragan Jevtić (član povjerenstva)
Committee memberSiniša Sovilj (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-09-23, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Rad se bavi temom detekcije anomalija u velikim podacima. Opisuje velike podatke, njihova svojstva i probleme u radu s njima. Također opisuje tokove podataka, njihove modele i načine obrade. Opisane su i anomalije u tokovima podataka, kako se dijele, koji su im uzroci i kako se otkrivaju. Opisana je arhitektura implementiranog sustava za detektiranje anomalija u podatkovnim tokovima. Implementirano rješenje koristi nekoliko tehnologija: FastAPI, Kafku, Druid i Sherlock. Sustav je ispravno detektirao anomalije na nekoliko različitih vrsta vremenskih nizova.
Abstract (english)
The paper deals with the topic of anomaly detection in big data. Describes big data, their properties and problems in working with them. The paper also describes data streams, their models and processing methods. Also anomalies in data flows, how they are classified, what their causes are, and how they are detected. The architecture of the implemented system for detecting anomalies in data flows is also described. The implemented solution uses several technologies: FastAPI, Kafka, Druid and Sherlock. Due to technical difficulties, the solution did not fully work.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:248804
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-01-14 10:01:11