In the last two decades, the continuous increase of computational power has produced an
overwhelming flow of data which has called for a paradigm shift in the computing …

MapReduce is a framework for processing and managing large-scale datasets in a
distributed cluster, which has been used for applications such as generating search indexes …

We present a new solution for finding joinable tables in massive data lakes: given a table
and one join column, find tables that can be joined with the given table on the largest …

Enterprises today acquire vast volumes of data from different sources and leverage this
information by means of data analysis to support effective decision-making and provide new …

k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for
every object in another dataset R, is a primitive operation widely adopted by many data …

Set similarity joins compute all pairs of similar sets from two collections of sets. We conduct
extensive experiments on seven state-of-the-art algorithms for set similarity joins. These …

String similarity search and join are two important operations in data cleaning and
integration, which extend traditional exact search and exact join operations in databases by …

As there is an increasing trend of applications being expected to deal with big data that
usually do not fit in the main memory of a single machine, analyzing big data is a …

String similarity join is an important operation in data integration and cleansing that finds
similar string pairs from two collections of strings. More than ten algorithms have been …

String similarity join is an essential operation in data integration. The era of big data calls for
scalable algorithms to support large-scale string similarity joins. In this paper, we study …