A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses

A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses

Hamid Haidarian Shahri (University of Maryland, USA)
DOI: 10.4018/978-1-60566-232-9.ch003
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Abstract

Entity resolution (also known as duplicate elimination) is an important part of the data cleaning process, especially in data integration and warehousing, where data are gathered from distributed and inconsistent sources. Learnable string similarity measures are an active area of research in the entity resolution problem. Our proposed framework builds upon our earlier work on entity resolution, in which fuzzy rules and membership functions are defined by the user. Here, we exploit neuro-fuzzy modeling for the first time to produce a unique adaptive framework for entity resolution, which automatically learns and adapts to the specific notion of similarity at a meta-level. This framework encompasses many of the previous work on trainable and domain-specific similarity measures. Employing fuzzy inference, it removes the repetitive task of hard-coding a program based on a schema, which is usually required in previous approaches. In addition, our extensible framework is very flexible for the end user. Hence, it can be utilized in the production of an intelligent tool to increase the quality and accuracy of data.
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Introduction

The problems of data quality and data cleaning are inevitable in data integration from distributed operational databases and online transaction processing (OLTP) systems (Rahm & Do, 2000). This is due to the lack of a unified set of standards spanning over all the distributed sources. One of the most challenging and resource-intensive phases of data cleaning is the removal of fuzzy duplicate records. Considering the possibility of a large number of records to be examined, the removal requires many comparisons and the comparisons demand a complex matching process.

The term fuzzy duplicates is used for tuples that are somehow different, but describe the same real-world entity, that is, different syntaxes but the same semantic. Duplicate elimination (also known as entity resolution) is applicable in any database, but critical in data integration and analytical processing domains, where accurate reports and statistics are required. The data cleaning task by itself can be considered as a variant of data mining. Moreover, in data mining and knowledge discovery applications, cleaning is required before any useful knowledge can be extracted from data. Other application domains of entity resolution include data warehouses, especially for dimension tables, online analytical processing (OLAP) applications, decision support systems, on-demand (lazy) Web-based information integration systems, Web search engines, and numerous others. Therefore, an adaptive and flexible approach to detect the duplicates can be utilized as a tool in many database applications.

When data are gathered form distributed sources, differences between tuples are generally caused by four categories of problems in data, namely, the data are incomplete, incorrect, incomprehensible, or inconsistent. Some examples of the discrepancies are spelling errors; abbreviations; missing fields; inconsistent formats; invalid, wrong, or unknown codes; word transposition; and so forth as demonstrated using sample tuples in Table 1.

Table 1.
Examples of various discrepancies in database tuples
Discrepancy ProblemNameAddressPhone NumberID NumberGender
John DowLucent Laboratories615 5544553066Male
Spelling ErrorsJohn DoeLucent Laboratories615 5544553066Male
AbbreviationsJ. DowLucent Lab.615 5544553066Male
Missing FieldsJohn Dow-615 5544-Male
Inconsistent FormatsJohn DowLucent Laboratories(021)61555445530661
Word TranspositionDow JohnLucent Laboratories615 5544553066Male

Complete Chapter List

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Table of Contents
Preface
David Taniar
Chapter 1
Laila Niedrite, Maris Solodovnikova Treimanis, Liga Grundmane
There are many methods in the area of data warehousing to define requirements for the development of the most appropriate conceptual model of a data... Sample PDF
Development of Data Warehouse Conceptual Models: Method Engineering Approach
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Chapter 2
Stefano Rizzi
In the context of data warehouse design, a basic role is played by conceptual modeling, that provides a higher level of abstraction in describing... Sample PDF
Conceptual Modeling Solutions for the Data Warehouse
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Chapter 3
Hamid Haidarian Shahri
Entity resolution (also known as duplicate elimination) is an important part of the data cleaning process, especially in data integration and... Sample PDF
A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses
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Chapter 4
Maurizio Pighin, Lucio Ieronutti
Data Warehouses are increasingly used by commercial organizations to extract, from a huge amount of transactional data, concise information useful... Sample PDF
Interactive Quality-Oriented Data Warehouse Development
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Chapter 5
Dirk Draheim, Oscar Mangisengi
Nowadays tracking data from activity checkpoints of unit transactions within an organization’s business processes becomes an important data resource... Sample PDF
Integrated Business and Production Process Data Warehousing
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Chapter 6
Jorge Loureiro, Orlando Belo
OLAP queries are characterized by short answering times. Materialized cube views, a pre-aggregation and storage of group-by values, are one of the... Sample PDF
Selecting and Allocating Cubes in Multi-Node OLAP Systems: An Evolutionary Approach
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Chapter 7
Jorge Loureiro, Orlando Belo
Globalization and market deregulation has increased business competition, which imposed OLAP data and technologies as one of the great enterprise’s... Sample PDF
Swarm Quant' Intelligence for Optimizing Multi-Node OLAP Systems
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Chapter 8
Franck Ravat, Olivier Teste, Ronan Tournier
With the emergence of Semi-structured data format (such as XML), the storage of documents in centralised facilities appeared as a natural adaptation... Sample PDF
Multidimensional Anlaysis of XML Document Contents with OLAP Dimensions
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Chapter 9
Hanene Ben-Abdallah, Jamel Feki, Mounira Ben Abdallah
Despite their strategic importance, the wide-spread usage of decision support systems remains limited by both the complexity of their design and the... Sample PDF
A Multidimensional Pattern Based Approach for the Design of Data Marts
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Chapter 10
Concepción M. Gascueña, Rafael Guadalupe
The Multidimensional Databases (MDB) are used in the Decision Support Systems (DSS) and in Geographic Information Systems (GIS); the latter locates... Sample PDF
A Multidimensional Methodology with Support for Spatio-Temporal Multigranularity in the Conceptual and Logical Phases
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Chapter 11
Francisco Araque, Alberto Salguero, Cecilia Delgado
One of the most complex issues of the integration and transformation interface is the case where there are multiple sources for a single data... Sample PDF
Methodology for Improving Data Warehouse Design using Data Sources Temporal Metadata
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Chapter 12
Shi-Ming Huang, John Tait, Chun-Hao Su, Chih-Fong Tsai
Data warehousing is a popular technology, which aims at improving decision-making ability. As the result of an increasingly competitive environment... Sample PDF
Using Active Rules to Maintain Data Consistency in Data Warehouse Systems
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Chapter 13
Marcin Gorawski, Wojciech Gebczyk
This chapter describes realization of distributed approach to continuous queries with kNN join processing in the spatial telemetric data warehouse.... Sample PDF
Distributed Approach to Continuous Queries with kNN Join Processing in Spatial Telemetric Data Warehouse
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Chapter 14
Maria Luisa Damiani, Stefano Spaccapietra
This chapter is concerned with multidimensional data models for spatial data warehouses. Over the last few years different approaches have been... Sample PDF
Spatial Data Warehouse Modelling
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Chapter 15
Jérôme Darmont
Performance evaluation is a key issue for designers and users of Database Management Systems (DBMSs). Performance is generally assessed with... Sample PDF
Data Warehouse Benchmarking with DWEB
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Chapter 16
Lars Frank, Christian Frank
A Star Schema Data Warehouse looks like a star with a central, so-called fact table, in the middle, surrounded by so-called dimension tables with... Sample PDF
Analyses and Evaluation of Responses to Slowly Changing Dimensions in Data Warehouses
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About the Contributors