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master's thesis
Raspodijeljeni modeli prostorno-vremenski ponderirane i grupirane regresije korištenjem platforme Apache Spark
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:496608
Britvec, Darko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Britvec, D. (2021). Raspodijeljeni modeli prostorno-vremenski ponderirane i grupirane regresije korištenjem platforme Apache Spark (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:496608

Britvec, Darko. "Raspodijeljeni modeli prostorno-vremenski ponderirane i grupirane regresije korištenjem platforme Apache Spark." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:496608

Britvec, Darko. "Raspodijeljeni modeli prostorno-vremenski ponderirane i grupirane regresije korištenjem platforme Apache Spark." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:496608

Britvec, D. (2021). 'Raspodijeljeni modeli prostorno-vremenski ponderirane i grupirane regresije korištenjem platforme Apache Spark', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:496608

Britvec D. Raspodijeljeni modeli prostorno-vremenski ponderirane i grupirane regresije korištenjem platforme Apache Spark [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 December 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:496608

D. Britvec, "Raspodijeljeni modeli prostorno-vremenski ponderirane i grupirane regresije korištenjem platforme Apache Spark", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:496608

Metadata
TitleRaspodijeljeni modeli prostorno-vremenski ponderirane i grupirane regresije korištenjem platforme Apache Spark
Title (english)Distributed Models of Geo-Temporal Weighted and Clustered Regression using Apache Spark
AuthorDarko Britvec
MentorKrešimir Pripužić (mentor)
Committee memberKrešimir Pripužić (predsjednik povjerenstva)
Committee memberSiniša Sovilj (član povjerenstva)
Committee memberJurica Babić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-09, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Analiza i ekstrakcija znanja iz velikih prostorno-vremenskih skupova podataka sve su više zastupljeni kako u istraživačkim radovima tako i u stvarnim programskim rješenjima. U ovom radu analizirani su modeli strojnog učenja koji iskorištavaju postojanje prostorne i vremenske heterogenosti u podacima. U radu je implementiran raspodijeljeni model prostorno-vremenski ponderirane regresije (GTWR) korištenjem platforme Apache Spark. Arhitektura tog modela ne omogućava efektivnu stvarnovremensku obradu velikih skupova podataka u računalnom grozdu. Stoga je u radu predložen te implementiran raspodijeljeni model prostorno-vremenski grupirane regresije (GTCR) koji umjesto postupka ponderiranja koristi algoritam grupiranja kako bi opisao prostornu i vremensku nestacionarnost podataka. Naposljetku, modeli su isprobani i uspoređeni na stvarnom skupu podataka koji opisuje koncentracije najučestalijih onečišćivača zraka sa 12 mjernih postaja u Pekingu kroz period od 2013. do 2017. godine.
Abstract (english)
Analysis and data mining performed on big spatio-temporal datasets is more and more frequent topic both in research and the software development. Machine learning models analysed in this paper take advantage of the spatio-temporal heterogeneity in the data. The paper covers the implementation of the Geo-Temporally Weighted Regression (GTWR) distributed machine learning model using the Apache Spark platform. This model does not support the effective near-real-time computation in cluster. Geo-Temporally Clustered Regression (GTCR) model was introduced in this paper to accommodate the need of the machine learning model which can describe the spatio-temporal non-stationarity in large datasets. Finally, models are tested and compared using the real-world dataset which consists of measurements of most common air pollutants from 12 stations in Beijing in a period from 2013 to 2017.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:496608
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 on2023-01-13 11:53:26