Renata Borovica-Gajic holds a position of Lecturer in Data Analytics in the School of Computing and Information Systems at The University of Melbourne. Dr Borovica-Gajic received her PhD degree in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland in 2016.
During her studies she worked in the Data-Intensive Applications and Systems Laboratory (DIAS), supervised by Prof. Anastasia Ailamaki. Prior to joining EPFL, she worked in industry for 5 years as a senior member of the database team of a power engineering company. She simultaneously completed her Master’s studies in Electrical and Computer Engineering, receiving a Serbian national award for the best Master’s thesis in the field of mathematics and computer science.
Renata’s research focuses on solving data management problems, when storing, accessing and processing massive data sets, enabling faster, more predictable, and cheaper data analysis as a result. She builds intelligent database systems that are able to automatically adjust query processing strategies to comply with the characteristics of data, hardware and usage patterns. She is also interested in the topics of scientific data management, big data exploration, query optimisation, physical database design, and hardware-software co-design.
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Explaining Slow Database Query Response Times Using Machine Learning Techniques
Internal Research Grant
Displaying the most recent project by Renata Borovica-Gajic.
Displaying the 11 most recent scholarly works by Renata Borovica-Gajic.
Finding all nearest neighbors with a single graph traversal
Y Xu, J Qi, R Borovica-Gajic, L Kulik
Conference Proceedings | 2018 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
© Springer International Publishing AG, part of Springer Nature 2018. Finding the nearest neighbor is a key operation in data anal..
Smooth Scan: Statistics-oblivious access paths
R Borovica-Gajic, S Idreos, A Ailamaki, M Zukowski, C Fraser
Conference Proceedings | 2015 | 2015 IEEE 31st International Conference on Data Engineering
© 2015 IEEE. Query optimizers depend heavily on statistics representing column distributions to create efficient query plans. In m..
Honours, Awards and Fellowships
EPFL IC School achievement award
Best poster runner-up award at ICDE 2015
National award "Mileva Maric Einstein" for the best Master's thesis in the field of mathematics and computer science
Lecturer In Data Analytics
Computing And Information Systems
PhD in Computer Science
Swiss Federal Institute of Technology - Lausanne
Masters in Electrical and Computer Engineering
University of Novi Sad