We are currently looking for motivated postdoc, PhD students and master students in the areas of big data management and processing systems. If you are interested to join our group, send us your application including detailed CV and publications to sherif.sakr [at] ut.ee. Please use the title "Joining Data Systems Groups @ UT" for your email message.
Welcome to the Homepage of the Data Systems Research Group at University of Tartu.
The Data Systems Group (DSG), Founded by Prof. Sherif Sakr in 2018, conducts research and teaching that covers various aspects of the data management field (e.g., structured data, semi-structured data, graph data streaming data) with an emphasis on scalability mechanisms that effectively tackle the requirements of the modern application (e.g., IoT, Smart Cities, Blockchain, Health Informatics) on dealing with Big Data and performing efficient Big Data Analytics.
The aim of our group is to get engaged and contribute to building the next generation of efficient, scalable and insightful data systems. We love to build useful systems. If you have the same interest, please contact us.
We invite you to browse our website and learn more about our research and activities.
Our Tutorial “Declarative Languages for Big Streaming Data: A database Perspective” and short paper “DISGD: A Distributed Shared-nothing Matrix Factorization for Large Scale Online Recommender Systems” have been accepted in The 23nd International Conference on Extending Database Technology (EDBT'20).
Prof. Sherif Sakr (together with Prof. Angela Bonifati, Prof. Alexandru Iosup and Dr. Hannes Voigt) have successfully organized the Dagstuhl Seminar on Big Graph Processing Systems. The group members Dr. Riccardo Tomassini and Mohamed Ragab have actively attended and participated in the seminar.
Prof. Sherif Sakr has been awarded the 2019 Best Arab Scholars Award from Abdul Hammed Shoman Foundation.
Our paper "A Decision Support Framework for AutoML Systems: A Meta-Learning Approach" has been accepted in the 1st IEEE ICDM Workshop on Autonomous Machine Learning (AML 2019)