One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …

Entity Resolution (ER), a core task of Data Integration, detects different entity profiles that
correspond to the same real-world object. Due to its inherently quadratic complexity, a series …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

Any important data management and analytics tasks cannot be completely addressed by
automated processes. These tasks, such as entity resolution, sentiment analysis, and image …

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 …

A crowdsourcing system, such as the Amazon Mechanical Turk (AMT), provides a platform
for a large number of questions to be answered by Internet workers. Such systems have …

The gap between unstructured natural language and structured data makes it challenging to
build a system that supports using natural language to query large knowledge graphs. Many …

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 …

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 …