Multi-Source Event Detection


The MUSED project seeks to solve challenges in event detection and prediction in multi-source data streams in the context of Big Data.

MUSED aims at developing the framework and techniques necessary for effective and efficient detection and prediction of events from multiple and possibly heterogeneous streaming data sources. We expect to provide contributions on the following research topics: 

  1. Efficient and effective information analysis techniques that are able to detect and predict events in real-time when the sources of data come from multiple and heterogeneous streams. 
  2. Efficient algorithms and structures for indexing and storage to support highly scalable multi-source event detection and prediction.

Learn more about the MUSED project.


Associate Professor Heri Ramampiaro
Project Leader
Heri Ramampiaro


MUSED aims to disseminate its research results to the scientific community through scientific publications at the top venues of our research area.

View MUSED publications

Latest News

  • September 30th, 2016: The project offers one fully financed Postdoc fellowship.
  • July 1st, 2016: The project offers one fully financed PhD fellowship.
  • February 23rd, 2016: The MUSED Project is officially initiated.


Kjetil Nørvåg Krisztian Balog Christos Doulkeridis Michael J. Carey Chen Li N. N. (from May 1st)N. N. (vacant PhD position)

MUSED People

Strategic Research Area

The MUSED project is one of several projects of the BigData strategic research area, hosted by the Faculty of Information Technology, Mathematics and Electrical Engineering.