PETGraphDB: A Property Evolution Temporal Graph Data Management System

2025-12-04

AI Summary

Existing management solutions for temporal graphs—characterized by frequently evolving attributes and relatively stable topology—suffer from complex modeling and poor query performance. This paper proposes an efficient management framework for attribute-evolving temporal graphs, built upon an effective-time model to construct a lightweight temporal property graph. We design a high-space-utilization compressed storage structure and a fine-grained multi-level locking mechanism, jointly ensuring ACID compliance and low-latency querying. Innovatively integrating HTAP (Hybrid Transactional/Analytical Processing) transaction processing with temporal property optimization techniques, our framework achieves substantial improvements in dynamic IoT graph scenarios: it reduces storage footprint to just 33% of the state-of-the-art approach, boosts average transaction throughput by 58.8×, and cuts query latency by up to 267×.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Temporal Planning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Manages property evolution temporal graphs with frequent property changes
• Addresses complex data modeling and low query performance issues
• Provides efficient storage and high transaction throughput for temporal graphs

Innovation

• Valid-time temporal property graph data model for modeling
• Space-efficient temporal property storage for performance
• Fine-granularity multi-level locking mechanism for concurrency