Event detection on social media using transaction based stream processing engine

Çınar, Hüseyin Alper
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.
Citation Formats
H. A. Çınar, “Event detection on social media using transaction based stream processing engine,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Computer Engineering., Middle East Technical University, 2019.