Abstract
In the practical use of graph databases, storing graphs separately enhances maintainability, while integrating them into a unified graph facilitates advanced analytics. To address these dual needs, we present a GQL-compatible framework for creating graph views across multiple property graphs. Leveraging insights from commercial graph database environments and user-driven requirements, we identify two key graph transformations - merging and expansion - that significantly boost query efficiency. By incorporating these transformations into view generation, we effectively minimize redundant queries. Furthermore, we streamline view creation through implicit edge creation and automated property merging. To ensure views meet user expectations before full generation, we introduce a preview feature that uses sampling to effectively represent the graph structure. We assess the performance of GalaxyView across diverse workloads, with comprehensive experiments on real-world graphs demonstrating significant improvements in query performance and operational efficiency.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2025 IEEE 41st International Conference on Data Engineering, ICDE 2025 |
| Publisher | IEEE Computer Society |
| Pages | 4470-4482 |
| Number of pages | 13 |
| ISBN (Electronic) | 9798331536039 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 41st IEEE International Conference on Data Engineering, ICDE 2025 - Hong Kong, China Duration: 19 May 2025 → 23 May 2025 |
Publication series
| Name | Proceedings - International Conference on Data Engineering |
|---|---|
| ISSN (Print) | 1084-4627 |
| ISSN (Electronic) | 2375-0286 |
Conference
| Conference | 41st IEEE International Conference on Data Engineering, ICDE 2025 |
|---|---|
| Country/Territory | China |
| City | Hong Kong |
| Period | 19/05/25 → 23/05/25 |
Bibliographical note
Publisher Copyright:© 2025 IEEE.
Keywords
- graph database
- graph transformation
- graph view
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