Detecting and repairing dirty data is one of the perennial challenges in data analytics, and
failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few …

Massive amounts of data are available for the organization which will influence their
business decision. Data collected from the various resources are dirty and this will affect the …

Abstract Machine learning (ML) has become a vital part in many aspects of our daily life.
However, building well performing machine learning applications requires highly …

Abstract Machine learning is a powerful tool for gleaning knowledge from massive amounts
of data. While a great deal of machine learning research has focused on improving the …

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 …

Machine learning has become an essential tool for gleaning knowledge from data and
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …

Entity matching (EM) identifies data instances that refer to the same real-world entity, such
as (David Smith, UWMadison) and (DM Smith, UWM). This problem has been a long …

Data cleaning has played a critical role in ensuring data quality for enterprise applications.
Naturally, there has been extensive research in this area, and many data cleaning …

This paper presents the design of a read-optimized relational DBMS that contrasts sharply
with most current systems, which are write-optimized. Among the many differences in its …

In previous papers [SC05, SBC+ 07], some of us predicted the end of" one size fits all" as a
commercial relational DBMS paradigm. These papers presented reasons and experimental …