Abstract:
The growing need for managing extensive dynamic datasets has propelled graph processing and streaming to the forefront
of the data processing community. Coping with the sheer volume, intricacy, and continual evolution of data poses a challenge to
graph data processing systems, necessitating the development of innovative techniques for effective handling and analysis. This paper
underscores two pivotal aspects within Graph Processing Systems (GPS) that significantly impact overall system performance: 1) the
graph representation, encompassing the data structures storing vertices and edges, and 2) graph mutation protocols, outlining approaches
for assimilating and storing new graph updates, such as additions of edges and vertices. Given the irregularity of graph workloads and
the large scale of real-world graphs, face numerous challenges, requiring adept solutions to ensure both efficient storage and update
protocols. This dual imperative enables rapid analytics and streaming capabilities. Our paper aims to furnish a comprehensive overview
of diverse methodologies employed by researchers to surmount performance challenges inherent in the design GPS and streaming. Our
review highlights challenges linked to graph representation and update protocols, offering insight into researchers’ efforts to tackle these
issues. Conclusively, we identify research gaps in the domains of graph representation and mutation, emphasizing areas that remain
unexplored and unresolved.
