Computers understand very little of the meaning of human language. This profoundly limits
our ability to give instructions to computers, the ability of computers to explain their actions to …

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 …

The all-pairs-similarity-search (or similarity join) problem has been extensively studied for
text and a handful of other datatypes. However, surprisingly little progress has been made …

This chapter provides an overview of the data matching process, and describes the five
major steps involved in this process: data pre-processing (cleaning and standardisation) …

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 …

K-Nearest Neighbor Graph (K-NNG) construction is an important operation with many web
related applications, including collaborative filtering, similarity search, and many others in …

We present the first provably sublinear time hashing algorithm for approximate\emph
{Maximum Inner Product Search}(MIPS). Searching with (un-normalized) inner product as …

Entity resolution is central to data integration and data cleaning. Algorithmic approaches
have been improving in quality, but remain far from perfect. Crowdsourcing platforms offer a …

With the increasing amount of data and the need to integrate data from multiple data
sources, one of the challenging issues is to identify near-duplicate records efficiently. In this …

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 …