k nearest neighbours (kNN) queries are fundamental in many applications, ranging from
data mining, recommendation system and Internet of Things, to Industry 4.0 framework …

A variety of techniques for performing a spatial join are reviewed. Instead of just
summarizing the literature and presenting each technique in its entirety, distinct components …

This paper describes an efficient approach to record linkage. Given two lists of records, the
record-linkage problem consists of determining all pairs that are similar to each other where …

The similarity join has become an important database primitive for supporting similarity
searches and data mining. A similarity join combines two sets of complex objects such that …

Similarity join algorithms find pairs of objects that lie within a certain distance ε of each other.
Algorithms that are adapted from spatial join techniques are designed primarily for data in a …

A similarity join operation A BOWTIE epsiv B takes two sets of points A, B and a value epsiv
isin Ropf, and outputs pairs of points p isin A, q isin B, such that the distance D (p, q) les …

Given a query string Q, an edit similarity search finds all strings in a database whose edit
distance with Q is no more than a given threshold t. Most existing method answering edit …

The partition-based spatial-merge join (PBSM) of JM Patel and DJ DeWitt (1996) and the
size separation spatial join (S/sup 3/J) of N. Koudas and KC Sevcik (1997) are considered to …

The similarity join is an important database primitive which has been successfully applied to
speed up applications such as similarity search, data analysis and data mining. The …

An important but very expensive primitive operation of high-dimensional databases is the K-
Nearest Neighbor (KNN) similarity join. The operation combines each point of one dataset …