Doctoral defence: Mohamed Ragab “Bench-ranking: a prescriptive analysis approach for large knowledge graphs query workloads”

On 13 January at 14:15 Mohamed Ragab will defend his doctoral thesis “Bench-ranking: a prescriptive analysis approach for large knowledge graphs query workloads” to obtain the degree of Doctor of Philosophy (in Computer Science).

Supervisors:
Prof. Ahmed Awad, University of Tartu
Assoc. Prof. Riccardo Tommasini, Lyon University (France)

Opponents:
Prof. Ladjel Bellatreche, National Engineering School for Mechanics and Aerotechnics ISAE-ENSMA (France)
Assoc. Prof. Ester Zumpano, University of Calabria (Italy)

Summary
Leveraging relational Big Data (BD) processing frameworks to process large knowledge graphs yields a great interest in optimizing query performance. Modern BD systems are yet complicated data systems, where the configurations notably affect the performance. Benchmarking different frameworks and configurations provides the community with best practices for better performance. However, most of these benchmarking efforts are classified as descriptive and diagnostic analytics. Moreover, there is no standard for comparing these benchmarks based on quantitative ranking techniques. Moreover, designing mature pipelines for processing big graphs entails considering additional design decisions that emerge with the non-native (relational) graph processing paradigm. Those design decisions cannot be decided automatically, e.g., the choice of the relational schema, partitioning technique, and storage formats. Thus, in this thesis, we discuss how our work fills this timely research gap. Particularly, we first show the impact of those design decisions’ trade-offs on the BD systems’ performance replicability when querying large knowledge graphs. Moreover, we showed the limitations of the descriptive and diagnostic analyses of BD frameworks’ performance for querying large graphs. Thus, we investigate how to enable prescriptive analytics via ranking functions and Multi-Dimensional optimization techniques (called ”Bench-Ranking”). This approach abstracts out from the complexity of descriptive performance analysis, guiding the practitioner directly to actionable informed decisions.

The defence can also be followed in Zoom (Meeting ID: 932 8951 0379, Passcode: ati).