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.
News Archive
2020
Dr. Riccardo Tomassini has successfully completed the defense for his PhD thesis titled as "Velocity on the Web" at the University of Politecnico di Milano. Riccardo's Thesis has been graded with Cum Laude. Well done Riccardo! Best Congratulations!
2019
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)
Our paper "On the Interpretability of Machine Learning-Based Model for Predicting Hypertension" has been accepted in the BMC Medical Informatics and Decision Making Journal
Our paper "D2IA: Stream Analytics on User-Defined Event Intervals" authored by Ahmed Awad, Riccardo Tommasini, Mahmoud Kamel, Emanuele Della Valle, Sherif Sakr has been awarded CAiSE'19 best paper award. Congratulations Team!
Our Paper “DLBench: An Experimental Evaluation of Deep Learning Frameworks” has been accepted in IEEE International Congress on Big Data, Milan, Italy. The project code is available here.
Our Tutorial Proposal “An Outlook to Declarative Languages for Big Steaming Data” has been accepted in the 13th ACM International Conference on Distributed and Event‐based Systems (DEBS), Darmstadt, Germany.
Two papers for our group have been accepted in The 32th IEEE CBMS International Symposium on Computer-Based Medical Systems, Córdoba, Spain.
- Interpretability in HealthCare: A Comparative Study of Local Machine Learning Interpretability Techniques
- LDLCT: An Instance-based Framework for Lesion Detection on Lung CT Scans
The Data Systems Group has organized a UT Data Science Seminar on the topic of "The emerging requirements of new application generation for Big Data Analytics". The seminar program and recording are available online.
Prof. Sherif Sakr has been appointed as a Co-Chair for European Big Data Value Association (BDVA) TF6-Data Technology Architectures Group
Our paper “D2IA: Stream Analytics on User-Defined Event Intervals” has been accepted in The 31st International Conference on Advanced Information Systems Engineering (CAiSE 2019), Rome, Italy.
Our Tutorial Proposal “Linked Data: Querying, Reasoning, and Benchmarking” has been accepted in the 18th International Conference on Semantic Web (ISWC 2019), Auckland, NewZealand
Our paper “Big Streaming Systems: An Experimental Evaluation” has been accepted in The
DASC 2019 workshop, held on April 8th within the 35th IEEE International Conference on Data Engineering (ICDE 2019).
The Data Systems Group has signed a collaboration agreement with The Center for Informatics Science at Nile University, Egypt. Details of the agreement are available here.
2018
Our group has got 2 Demo Papers
- “SmartML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Machine Learning Algorithms ”
- “MINARET: A Recommendation Framework for Scientific Reviewers ”
and one short paper
- “A Concept Drift-based Approach for Predicting Event-Time Progress in Data Streams ”
accepted in The 22nd International Conference on Extending Database Technology (EDBT'19).
Our paper “Calculation of Average Road Speed Based on Car-to-Car Messaging” has been accepted in The 6th IEEE International Conference on Big Data and Smart Computing (BigComp'19).
University of Tartu has released the news about our research group and the potential of big data. Please check here.
Our paper “Business Process Analytics and Big Data Systems: A Roadmap to Bridge the Gap” has been accepted in IEEE Access Journal.
Our Senior Research Fellow, Dr. Radwa Elshawi, received funding for 2 years on the Mobilitas Pluss postdoctoral researcher grant from the Estonian Research Council (ETAg) . For more information about the project of Dr. Elshawi, please visit the announcment news
Our paper “Stream Processing Languages in the Big Data Era” and our report for the "Dagstuhl Seminar on Big Stream Processing" have been accepted in SIGMOD Record.
Our paper “Predictive Model for the Incidence of Hyperkalemia for Congestive Heart Failure Patients on Spironolactone” has been accepted in The 6th IEEE International Conference on Healthcare Informatics (ICHI 2018), NY, USA
Prof. Sakr has delivered IEEE Distinguished Speaker Talks on the IEEE Tunisia Sections (IEEE IoT Tunisia Forum, The Second BioDialog Summer School on Biodiversity Informatics)
Our paper “Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Service” has been accepted in The Journal of Big Data Research, Elsevier.
Our demo “HDM-MC in-Action: A Framework for Big Data Analytics across Multiple Clusters” has been accepted in the 38th IEEE International Conference on Distributed Computing Systems (ICDCS'18).
Our Paper “Using Machine Learning on Cardiorespiratory Fitness Data for Predicting Hypertension: The Henry Ford ExercIse Testing (FIT) Project” has been accepted in the PLOS ONE journal
Our Paper “Prognostic Value of Exercise Capacity among Patients with Treated Depression: The Henry Ford ExercIse Testing (FIT) Project” has been accepted in the Clinical Cardiology Journal.