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master's thesis
Detekcija anomalija u okruženju velikih podataka
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. urn:nbn:hr:168:179612
Dundović, Ivan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Dundović, I. (2017). Detekcija anomalija u okruženju velikih podataka (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:179612

Dundović, Ivan. "Detekcija anomalija u okruženju velikih podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:179612

Dundović, Ivan. "Detekcija anomalija u okruženju velikih podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:179612

Dundović, I. (2017). 'Detekcija anomalija u okruženju velikih podataka', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:179612

Dundović I. Detekcija anomalija u okruženju velikih podataka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2017 [cited 2024 November 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:179612

I. Dundović, "Detekcija anomalija u okruženju velikih podataka", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:168:179612

Metadata
TitleDetekcija anomalija u okruženju velikih podataka
Title (english)Anomaly Detection in Big Data Environments
AuthorIvan Dundović
MentorDamir Pintar (mentor)
Committee memberDamir Pintar (predsjednik povjerenstva)
Committee memberZoran Skočir (član povjerenstva)
Committee memberBoris Vrdoljak (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2017-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Anomalije su pojave koje su neobične i odstupaju od zakonitosti, pravila i uobičajenog. Detekcija anomalija ima primjenu u mnogim područjima, između ostalog i u kontekstu detekcije prijevara. U okruženju velikih podataka otvaraju se novi problemi koji se ne mogu riješiti primjenom tradicionalnih alata i tehnologija. Primjenom tehnologija za rad s velikim podacima, poput Apache Sparka, mnogi takvi problemi postaju rješivi i otvaraju se nove mogućnosti za detekciju anomalija. Prije svega, omogućuju obradu puno veće količine podataka i samim time pružaju više znanja o podacima. U radu je napravljena detekcija anomalija nad podacima o telefonskim pozivima koristeći Apache Spark. Za detekciju anomalija korištene su dvije metode: z-ocjena i algoritam k-srednjih vrijednosti. Osim Apache Sparka koji je korišten za obradu podataka i detekciju anomalija, korišten je programski jezik R za vizualizaciju i analizu konačnih rezultata.
Abstract (english)
Anomalies are observations that are unusual and deviate from rules and normal observations. Anomaly detection is applicable in numerous fields, especially in fraud detection.There are many problems in big data environments that cannot be solved using traditional tools and technologies. With the use of big data technologies, such as Apache Spark, many of those problems could be solved and new opportunities for anomaly detection would be made available. Those technologies allow for processing of big data while simultaneously increasing understanding of the data itself. In this thesis anomaly detection is done using Apache Spark on telephone calls data. For anomaly detection two methods have been used: z-score and k-means algorithm. Besides Apache Spark, which is used primarily for data processing and anomaly detection, R programming language is used for visualization and results analysis.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:179612
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
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Created on2019-03-13 20:05:10