Title

EVENT DETECTION ON SOCIAL MEDIA USING TRANSACTION BASED STREAM PROCESSING ENGINE

Abstract

Abstract

The aim of this study is detecting events on social media with improving current solutions by means 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 keyword-based event detection method that looks for bursty keywords in a period of time. The second one is clustering-based event detection method which is a basically an version of hierarchical clustering algorithm. And the last one is 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 event detection methods are run and evaluated their performance with a real data set obtained from Twitter.

Biography:

Supervisor(s)

Supervisor(s)

HUSEYIN ALPER CINAR

Date and Location

Date and Location

2019-06-10;13:30:00-A101

Category

Category

MSc_Thesis