|CHAUDHRY HASSAN NAZEER||Cycle: XXXII |
Section: Computer Science and Engineering
Tutor: PRADELLA MATTEO
Advisor: ROSSI MATTEO GIOVANNI Major Research topic
:Efficient processing of graph-based data streamsAbstract:
Graph data structures model relations between entities in many diverse application domains. Graph processing systems enable scalable distributed computations over large graphs but are limited to static scenarios in which the structure of the graph does not change. However, virtually all applications are dynamic in nature, and this results in graphs that continuously evolve over time. Understanding the evolution of graphs is key to enable timely reactions when necessary. During my Ph.D. work, I addressed this problem by proposing a new model to express temporal patterns over graph data structures. The model seamlessly integrates computations over graphs to extract relevant values and temporal operators that define patterns of interest in the evolution of the graph.
During my research, I developed the syntax and semantics of this model and discussed its concrete implementation in a framework called FlowGraph, a middleware for temporal pattern recognition in large scale graphs. The performance and scalability of FlowGraph are thoroughly evaluated with various workloads and use cases. FlowGraph presents a level of performance that is comparable to any state-of-the-art graph processing tool which processes static graphs. In the presence of temporal patterns, it can further optimize processing by avoiding complex graph computations until strictly necessary for pattern evaluation.