Geoprostorno praćenje područja s pojačanim interesom za odabranu temu temeljeno na analizi podataka s društvenih mreža

master's thesis

• Title (english): Geospatial Monitoring of Areas with an Increased Interest in a Selected Topic Based on the Analysis of Social Media Data
• Author: Jasmin Redžepović
• Mentor: Ljiljana Brkić (mentor)
• Committee member: Ljiljana Brkić (predsjednik povjerenstva)
• Committee member: Vedran Mornar (član povjerenstva)
• Committee member: Boris Milašinović (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2019-07-03, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

U radu su opisani principi rada s tokovima podataka u stvarnom vremenu. Opisana su osnovna svojstva koja aplikacije koje obrađuju tokove podataka trebaju imati. Kao izvor podataka u stvarnom vremenu odabran je Twitter StreamingAPI te su opisane njegove najbitnije značajke. Zatim su uspoređeni radni okviri Apache Spark i Apache Flink te je napravljen eksperiment kojim se utvrđuje kašnjenje obrade događaja u tim radnim okvirima u ovisnosti o količini generiranih događaja. Na kraju je implementiran sustav koji je u mogućnosti u stvarnom vremenu geografski pratiti objave s društvene mreže Twitter. Implementirani sustav sastoji se od dvije glavne komponente. Jedna vrši obradu u stvarnom vremenu, a druga povijesnu obradu. Na kraju su izrađene geoprostorne vizualizacije pomoću kojih se mogu lako utvrditi trendovi u obrađenim podatcima.

Abstract (english)

This paper describes main principles to follow when dealing with real time data streams. As a source of real time data Twitter StreamingAPI was chosen and main features were described. Later, frameworks Apache Spark and Apache Flink were compared and benchmark was performed over them. That benchmark included measuring processing latency depending on data throughput. In the end, there is description of a system which was implemented to perform real time geospatial monitoring of data from Twitter. That system is contained of two main components. One component performs real time analysis over data stream coming from Twitter while the other component performs analysis over historical Twitter data. Also, geospatial visualizations were implemented which can easily show trends in analysed data.

• Keywords: tokovi podataka; Apache Spark; Apache Kafka; Apache Flink; Twitter
• Keywords (english): data streams; Apache Spark; Apache Kafka; Apache Flink; Twitter
• Language: croatian
• URN:NBN: urn:nbn:hr:168:959680
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2020-01-07 21:47:57