Abstract K Nearest Neighbor (KNN) joins are used in scientific domains for data analysis,
and are building blocks of several well-known algorithms. KNN-joins find the KNN of all …

The self-join finds all objects in a dataset within a threshold of each other defined by a
similarity metric. As such, the self-join is a fundamental building block for the field of …

To overcome the high computational cost associated with the high-dimensional digital
image descriptor matching, this paper presents a set of integrated parallel algorithms for the …

The similarity search problem is found in many application domains including computer
graphics, information retrieval, statistics, computational biology, and scientific data …

We introduce Hybrid-Dbscan, that uses the GPU and CPUs for optimizing clustering
throughput. The main idea is to exploit the memory bandwidth on the GPU for fast index …

Applications in many domains perform searches over datasets that contain moving object
trajectories. A common class of searches are similarity searches that attempt to identify …

Given two datasets (or tables) A and B and a search distance ϵ ϵ, the distance similarity
join, denoted as A\ltimes _ ϵ BA⋉ ϵ B, finds the pairs of points (p_a pa, p_b pb), where p_a …

We present a novel multi-dimensional range query co-processing scheme for the CPU and
GPU. It has been reported that traversing hierarchical tree structures in parallel is inherently …

Large datasets in astronomy and geoscience often require clustering and visualizations of
phenomena at different densities and scales in order to generate scientific insight. We …

Numerous research efforts have been proposed for efficient processing of range queries in
high-dimensional space by either redesigning R-tree access structure for exploring massive …