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master's thesis
Analiza velikih skupova podataka primjenom algoritama dubinske analize
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. urn:nbn:hr:168:292606
Medaković, Nikola
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Medaković, N. (2014). Analiza velikih skupova podataka primjenom algoritama dubinske analize (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:292606

Medaković, Nikola. "Analiza velikih skupova podataka primjenom algoritama dubinske analize." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. https://urn.nsk.hr/urn:nbn:hr:168:292606

Medaković, Nikola. "Analiza velikih skupova podataka primjenom algoritama dubinske analize." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. https://urn.nsk.hr/urn:nbn:hr:168:292606

Medaković, N. (2014). 'Analiza velikih skupova podataka primjenom algoritama dubinske analize', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 April 2024, https://urn.nsk.hr/urn:nbn:hr:168:292606

Medaković N. Analiza velikih skupova podataka primjenom algoritama dubinske analize [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2014 [cited 2024 April 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:292606

N. Medaković, "Analiza velikih skupova podataka primjenom algoritama dubinske analize", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2014. Available at: https://urn.nsk.hr/urn:nbn:hr:168:292606

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Metadata
TitleAnaliza velikih skupova podataka primjenom algoritama dubinske analize
Title (english)Big Data Analytics by Using Data Mining Algorithms
AuthorNikola Medaković
MentorBoris Vrdoljak (mentor)
Committee memberBoris Vrdoljak (predsjednik povjerenstva)
Committee memberIgor Mekterović (član povjerenstva)
Committee memberMarko Banek (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2014-07-07, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Sve veća količina pohranjivanih podataka donosi sa sobom nove izazove kada se govori o njihovoj analizi zbog njihove rastuće količine, brzine generiranja i raznolikosti. Twitter je društvena mreža čiji su podaci o interakcijama korisnika primjer Velikog skupa podataka. Kroz njega je lako pratiti proces propagacije informacija kroz mrežu, što uvelike olakšava proučavanje društvenog utjecaja korisnika. Društveni utjecaj se u ovom kontekstu može definirati kao sposobnost korisnika da svojim djelovanjem utječe na druge korisnike. U ovom radu su se dohvatili podaci o korisnicima i njihovim objavama na temu grafičkih kartica, a iz njih su se generirale mreže sljedbenika, spominjanja i ponovnog objavljivanja. Te mreže su se predale algoritmima Pagerank, HITS, centralnosti temeljene na međupovezanosti i centralnosti temeljene na bliskosti. Rezultati analize mreže sljedbenika su potvrdili navode iz literature, dok se za druge dvije mreže pokazalo da su premalene odnosno da se je potrebno sakupljati podatke dulje vrijeme kako bi se došlo do statistički značajnog udjela podataka.
Abstract (english)
Lately, more and more data is getting stored and analyzing it brings with itself new challenges because of its volume, velocity and variety. Twitter is a social network that is a good example of a Big Data source. Through Twitter it is very easy to monitor the information propagation process and that ease greatly improves the ability to study social influence. In this context, social influence can be defined as the ability of a user to influence other users in any way. In this thesis Twitter data was gathered about tweets related to graphics cards as well as data about their authors. That data was used to generate networks of followers, mentions and retweets which were passed to the Pagerank, HITS, closeness and betweenness centrality algorithms. The results of the follower network analysis confirmed what was stated in many of the related articles, while the analysis of the other two networks failed to yield any meaningful results since the amount of gathered data was too small and should be increased if the data sample is to become statistically significant.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:292606
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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RepositoryFER Repository
Created on2018-02-06 10:34:51

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