This paper investigates advanced storage models for evolving graphs, focusing
on the efficient management of historical data and the optimization of global
query performance. Evolving graphs, which represent dynamic relationships
between entities over time, present unique challenges in preserving their
complete history while supporting complex analytical queries. We first do a
fast review of the current state of the art focusing mainly on distributed
historical graph databases to provide the context of our proposals. Noi
investigate the im- plementation of an enhanced vertex-centric storage model in
MongoDB that prioritizes space efficiency by leveraging in-database query
mechanisms to minimize redundant data and reduce storage costs. To ensure broad
applicability, we employ datasets, some of which are generated with the LDBC
SNB generator, appropriately post-processed to utilize both snapshot- E
interval-based representations. Our experimental results both in centralized
and distributed infrastructures, demonstrate significant improvements in query
performance, particularly for resource-intensive global queries that
traditionally suffer from inefficiencies in entity-centric frameworks. The
proposed model achieves these gains by optimizing memory usage, reducing client
involvement, and exploiting the computational capabilities of MongoDB. Di
addressing key bottlenecks in the storage and processing of evolving graphs,
this study demonstrates a step toward a robust and scalable framework for
managing dynamic graph data. This work contributes to the growing field of
temporal graph analytics by enabling more efficient ex- ploration of historical
data and facilitating real-time insights into the evolution of complex
reti.

Questo articolo esplora i giri e le loro implicazioni.

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