Given a query graph, top-k subgraph matching finds up to$k$matches in a data graph with the highest scores according to a user-defined scoring function. It has wide applications across many fields, including knowledge graphs and social networks. Due to the enormous search space, existing methods are not efficient enough on large graphs. In this paper, we proposePTAB, an efficient serial algorithm for top-k subgraph matching. It traverses an efficiently pruned search space by topology-aware subspace score upper bounds computed from a novel hop index, which stores the range of node properties in a constrained multi-hop neighborhood of each node. Additionally,PTABintegrates a cost-aware root selection strategy, which chooses query nodes leading to a search process that utilizes the pruning power of the hop index as much as possible. Furthermore, we use a novel edge-cut strategy to handle general query graphs with cycles. We also extendPTABto a parallel version,PTABP, which further improves performance via multi-threading and enhanced pruning techniques. Experimental results on real and synthetic datasets demonstrate that our methods outperform existing methods.

Given a query graph, top-k subgraph matching finds up to k matches in a data graph with the highest scores according to a user-defined scoring function. It has wide applications across many fields, including knowledge graphs and social networks. Due to the enormous search space, existing methods are not efficient enough on large graphs. In this paper, we propose PTAB, an efficient algorithm for top-k subgraph matching. It traverses an efficiently pruned search space by topology-aware sub-space score upper bounds computed from a novel hop index, which stores the range of node properties in a constrained multi-hop neighborhood of each node. Additionally, PTAB integrates a cost-aware root selection strategy, which chooses query nodes leading to a search process that utilizes the pruning power of the hop index as much as possible. Furthermore, we use a novel edge-cut strategy to handle general query graphs with cycles. Experimental results on real and synthetic datasets demonstrate that our method outperforms existing methods.

A regular path query (RPQ) returns node pairs connected by a path whose edge label sequence satisfies the given regular expression. Given a workload of RPQs, selecting the shared subqueries as materialized views to precompute offline can speed up the online processing. Since the available memory is limited, we define the materialized view selection (MVS) problem for RPQs as minimizing the total workload query cost within a memory budget. To tackle the problem's NP-hardness, we design an efficient MVS algorithm based on heuristics. To prevent redundancies in the selected views, we devise the AND-OR directed acyclic graph with closure (AODC) as the multi-RPQ query plan representation for the workload, which encodes the relations between subqueries. In addition to detecting view redundancy, the AODC also incrementally updates itself during view selection. To support query planning, we design a scalable cost and cardinality estimation scheme for full-fledged RPQs, including Kleene closures. Our method, when applied to the Wikidata Query Logs, shows a 9.73× speedup in the total query processing time compared to ad-hoc processing, using the views it selects.

Query optimization is an especially challenging problem in graph databases due to its wide plan space and the difficulty in gathering statistics. In this demonstration, we propose a new cost-based query optimizer called gCBO for graph databases and implement it in a specific graph database (i.e., gStore). To tackle the aforementioned challenges, gCBO employs a hybrid plan enumerator based on dynamic programming, cost models that capture the characteristics of different types of joins, and a sampling-based cardinality estimation strategy that gathers the necessary statistics on-the-fly. What is more, to absorb the experience of users, we build an interactive component for gCBO, which allows users to receive the optimized execution plans with detailed information and generate their own plans for execution.