Supporting Window Analytics over Large-scale Dynamic Graphs
Qi Fan, Zhengkui Wang, Chee-Yong Chan, Kian-Lee Tan

TL;DR
This paper introduces graph window queries, formalizes their processing, and develops novel indices that significantly improve performance for large-scale dynamic graph analytics.
Contribution
It formally defines graph window functions, proposes two new indices for efficient processing, and demonstrates substantial performance improvements over existing methods.
Findings
Index-based solutions achieve up to four orders of magnitude performance gain.
Proposed indices outperform existing algorithms in scalability and efficiency.
Effective processing of large-scale dynamic graph window queries is demonstrated.
Abstract
In relational DBMS, window functions have been widely used to facilitate data analytics. Surprisingly, while similar concepts have been employed for graph analytics, there has been no explicit notions of graph window analytic functions. In this paper, we formally introduce window queries for graph analytics. In such queries, for each vertex, the analysis is performed on a window of vertices defined based on the graph structure. In particular, we identify two instantiations, namely the k-hop window and the topological window. We develop two novel indices, Dense Block index (DBIndex) and Inheritance index (I-Index), to facilitate efficient processing of these two types of windows respectively. Extensive experiments are conducted over both real and synthetic datasets with hundreds of millions of vertices and edges. Experimental results indicate that our proposed index-based query…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Management and Algorithms · Advanced Database Systems and Queries · Graph Theory and Algorithms
