We classify data quality problems that are addressed by data cleaning and provide an
overview of the main solution approaches. Data cleaning is especially required when …

Semantic integration has been a long-standing challenge for the database community. It has
received steady attention over the past two decades, and has now become a prominent area …

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 …

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) …

Irony is a pervasive aspect of many online texts, one made all the more difficult by the
absence of face-to-face contact and vocal intonation. As our media increasingly become …

Often, in the real world, entities have two or more representations in databases. Duplicate
records do not share a common key and/or they contain errors that make duplicate matching …

Using an open-source, Java toolkit of name-matching methods, we experimentally compare
string distance metrics on the task of matching entity names. We investigate a number of …

Many important problems involve clustering large datasets. Although naive implementations
of clustering are computationally expensive, there are established efficient techniques for …

The¡-means algorithm is by far the most widely used method for discovering clusters in data.
We show how to accelerate it dramatically, while still always computing exactly the same …

The rapid growth of the Web in the past two decades has made it the largest publicly
accessible data source in the world. Web mining aims to discover useful information or …