Individual Grants Programme Grant No. GR 4497/4 (2016-2018)

In this project, we propose to address the need for adaptive and scalable event detection in Twitter in the tradition of Data Stream Management Systems (DSMS) research.

In order to focus the project, we will concentrate on the specific task of first story detection, i.e., the detection of general (unknown) events, which is defined as one of the subtasks of TDT. We plan to address these issues in three separate work packages. In the first work package, we will study how event detection methods can adapt to the content of the stream by exploring better ways to segment the stream before it is processed and by adjusting method parameters during processing.

The second work package will address scalability requirements in terms of scaling up and down with the volume of one stream but also in terms of scaling up to several parallel streams. Finally, a third work package will be dedicated to the non-trivial task of evaluating event detection techniques.