PETGraphDB: A Property Evolution Temporal Graph Data Management System

TL;DR

This work targets efficient management of Property Evolution Temporal Graphs (PETGs) in IoT contexts by introducing PETGraphDB, a system with a native valid-time PETG data model, ACID transactions, and a TIM-Tree-based storage engine tailored for Entity-History queries and frequent Append writes. It couples a two-component architecture (database kernel and optional TCypher processor) with a fine-grained multi-level locking mechanism to achieve high HTAP throughput and dramatically reduced query latency. The authors validate PETGraphDB against PostgreSQL, MariaDB, and Neo4j across three real/synthetic PETG datasets, reporting substantially lower storage footprint (average 33% of a strong baseline), 58x higher HTAP throughput, and up to 267x faster temporal queries. The work contributes a coherent PETG data model, a scalable storage structure, optimized concurrency control, and a practical query language extension, offering a viable path for single-machine PETG management at industrial scales.

Abstract

Temporal graphs are graphs whose nodes and edges, together with their associated properties, continuously change over time. With the development of Internet of Things (IoT) systems, a subclass of the temporal graph, i.e., Property Evolution Temporal Graph, in which the value of properties on nodes or edges changes frequently while the graph's topology barely changes, is growing rapidly. However, existing temporal graph management solutions are not oriented to the Property Evolution Temporal Graph data, which leads to highly complex data modeling and low-performance query processing of temporal graph queries. To solve these problems, we developed PETGraph, a data management system for Property Evolution Temporal Graph data. PETGraph adopts a valid-time temporal property graph data model to facilitate data modeling, supporting ACID features with transactions. To improve temporal graph query performance, we designed a space-efficient temporal property storage and a fine-granularity multi-level locking mechanism. Experimental results show that PETGraph requires, on average, only 33% of the storage space needed by the current best data management solution. Additionally, it achieves an average of 58.8 times higher transaction throughput in HTAP workloads compared to the best current solutions and outperforms them by an average of 267 times in query latency.

Figures (8)

• Figure 1: Example of traffic network model by temporal property graph, including 2 intersections connecting 14 roads
• Figure 2: System architecture of $$PETGraphDB
• Figure 3: The structure of Time Interval Merge Tree
• Figure 4: Data organization of temporal property storage
• Figure 5: Evaluation of database disk space usage (GB).