Event detection on social media using transaction based stream processing engine

Date

2019

Author

Çınar, Hüseyin Alper

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The aim of this study is detecting events on social media by improving current solutions in terms of accuracy and time performance. An event is something that occurs in a short duration of time in a certain place. In this thesis, the problem is modelled as a streaming transaction process. Three different event detection method is adapted to our solution. First one is the keyword-based event detection method that looks for bursty keywords in a period. The second one is the clustering-based event detection method which is a version of the hierarchical clustering algorithm. And the last one is the hybrid event detection method of keyword-based and clustering-based algorithms. To specify the problem as streaming transaction process, all algorithms are implemented on top of S-Store. S-Store is a streaming OLTP engine having distributed, scalable and guaranteed ordered delivery features. All of the event detection methods are run and evaluated their performance with a real data set obtained from Twitter.

Subject Keywords

Event processing (Computer science)., Keywords: Online event detection, Streaming online transaction processing, Distributed systems, Keyword-based event detection, Clustering-based event detection, Twitter, S-Store

URI

http://etd.lib.metu.edu.tr/upload/12623323/index.pdf
https://hdl.handle.net/11511/43591

Collections

Graduate School of Natural and Applied Sciences, Thesis