The increasing demand for high-performance query processing on large in-memory datasets has driven the adoption of GPU-based B+-trees for handling high-concurrency query (HCQ) workloads. Existing approaches, by randomly assigning queries to GPU threads, suffer from inefficiencies related to memory access patterns, cache utilization, and thread divergence. This paper introduces a novel query grouping strategy that assigns queries with similar search keys to the same CUDA block, thereby improving query throughput. We formalize the optimal key assignment (OKA) problem as a variation of the K-means problem, establishing its theoretical foundations and proposing an efficient algorithm with proven optimality. We implement this algorithm using highly optimized CUDA code and extend our approach to support range queries, a common but understudied workload in GPU-based HCQ systems. Experimental evaluations demonstrate that our query grouping strategy significantly outperforms prior work, achieving up to 10.6X lower latency and 32.2X higher throughput, while also improving GPU resource utilization (e.g., cache hit rate and memory throughput).

Frequent subgraph mining (FSM) from graphs is an active subject in computer science research. One major challenge in FSM is the development of support measures, which are basically functions that map a pattern to its frequency count in a database. Current state-of-the-art in this topic features a hypergraph-based framework for modeling pattern occurrences which unifies the two main flavors of support measures: the overlap-graph based maximum independent set measure (MIS) and minimum image/instance based (MNI) measures. For the purpose of exploring the middle ground between these two groups and guiding the development of new support measures, we present general sufficient conditions for designing new support measures in hypergraph framework, which can be applied to MNI and other support measures that are not included in the overlap graph framework. We utilize the sufficient conditions to generalize MNI and minimum-instance measure (MI) for designing user-defined linear-time measures. Furthermore, we show that a maximum independent subedge set (MISS) measure developed from the sufficient conditions can fill the gap between MIS and MI in computation complexity and support count.

Frequent subgraph mining (FSM) from graphs is an active subject in computer science research. One major challenge in FSM is the development of support measures, which are basically functions that map a pattern to its frequency count in a database. Current state-of-the-art in this topic features a hypergraph-based framework for modeling pattern occurrences which unifies the two main flavors of support measures: the overlap-graph based maximum independent set measure (MIS) and minimum image/instance based (MNI) measures. For the purpose of exploring the middle ground between these two groups, we introduce a new polynomial-time support measure, called maximum independent subedge set (MISS) measure to fill the gap between MIS and MI in terms of computation complexity and support count. Bounding theorems among all relevant support measures are also presented in this paper.

In recent years, the popularity of graph databases has grown rapidly. This paper focuses on single-graph as an effective model to represent information and its related graph mining techniques. In frequent pattern mining in a single-graph setting, there are two main problems: support measure and search scheme. In this paper, we propose a novel framework for constructing support measures that brings together existing minimum-image-based and overlap-graph-based support measures. Our framework is built on the concept of occurrence / instance hypergraphs. Based on that, we present two new support measures: minimum instance (MI) measure and minimum vertex cover (MVC) measure, that combine the advantages of existing measures. In particular, we show that the existing minimum-image-based support measure is an upper bound of the MI measure, which is also linear-time computable and results in counts that are close to number of instances of a pattern. Although the MVC measure is NP-hard, it can be approximated to a constant factor in polynomial time. We also provide polynomial-time relaxations for both measures and bounding theorems for all presented support measures in the hypergraph setting. We further show that the hypergraph-based framework can unify all support measures studied in this paper. This framework is also flexible in that more variants of support measures can be defined and profiled in it.