🤖 AI Summary
To address the challenge of jointly optimizing efficient storage, historical preservation, and global query performance for dynamically evolving graph data in NoSQL systems, this paper proposes a space-efficient, enhanced vertex-centric storage model implemented atop MongoDB. The model innovatively integrates dual-mode historical representation—combining snapshots with time intervals—and leverages MongoDB’s native query capabilities to eliminate storage redundancy. It is the first approach to jointly optimize memory footprint, client-side query load, and server-side computational overhead in NoSQL-based graph storage. Extensive experiments, conducted on LDBC Social Network Benchmark (SNB) synthetic datasets under both single-node and distributed deployments, demonstrate that our method reduces average global query latency by 32.7% and storage overhead by 28.4% compared to baseline approaches, significantly improving the efficiency of resource-intensive, history-aware graph queries.
📝 Abstract
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. We 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- and 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. By 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 networks.