The volume, velocity, and variety of digital data is increasing day by day. It is expected that by 2025, the amount of data will exceed 163 zettabytes, which was 33 zettabytes in 2018. Therefore, organizations are increasingly pivoting towards leveraging big data storage and processing technologies (e.g., Hadoop, Spark, and Cassandra) to deal with the massive volume, velocity, and variety of data referred to as ‘big data'. Collecting, storing, analyzing, and visualizing big data in real-time is the need of the hour in several domains such as healthcare, cyber security, and traffic management. For instance, rapid response based on large-scale data analysis mitigates the repercussions of an emergency situation such as traffic accidents and natural disasters. Similarly, real-time detection of cyber-attacks mitigates the impact of the attack by 97%.
Within the area of big data analytics, CREST researchers leverage state-of-the-art techniques (e.g., AI and search-based optimization) to design, implement, deploy, and evaluate big data systems for optimally collecting, storing, analyzing, and visualizing a large volume of data in real-time. CREST research particularly focuses on the evaluation of big data storage solutions (e.g., Cassandra and MongDB) and big data analytical solutions (e.g., Spark and Flink) as deployed on private, public, and hybrid clouds. The application domains of our research on real-time big data analytics include but not limited to cyber security, oil and gas, and healthcare.
Applied Data Science knowledge and insights to support big data analysis across a variety of domains.
Applied cloud computing to provide optimized solution for big data analytics.
Automate the provision of multi-clouds infrastructures and deployment of multiple cluster-based applications.
Automate the selection and usage of big data technologies for users lacking big data expertise and resources
Faheem Ullah and Ali Babar. "Architectural tactics for big data cybersecurity analytics systems: a review." Journal of Systems and Software 151 (2019): 81-118.
Yaser Mansouri, Victor Prokhorenko, and Ali Babar. "An automated implementation of hybrid cloud for performance evaluation of distributed databases." Journal of Network and Computer Applications 167 (2020): 102740.
Faheem Ullah and Muhammad Ali Babar. "An Architecture-driven Adaptation Approach for Big Data Cyber Security Analytics." 2019 IEEE International Conference on Software Architecture (ICSA). IEEE, 2019.
Victor Prokhorenko and M. Ali Babar. "Architectural resilience in cloud, fog and edge systems: A survey." IEEE Access 8 (2020): 28078-28095.
Faheem Ullah and M. Ali Babar. QuickAdapt: Scalable Adaptation for Big Data Cyber Security Analytics. 2019 24th International Conference on Engineering of Complex Computer Systems (ICECCS). IEEE, 2019.
Yaser Mansouri and M. Ali Babar. The Impact of Distance on Performance and Scalability of Distributed Database Systems in Hybrid Clouds. arXiv preprint arXiv:2007.15826 (2020).
Ullah, Faheem, and M. Ali Babar. Quantifying the Impact of Design Strategies for Big Data Cyber Security Analytics: An Empirical Investigation. 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE, 2019.