Sign up. Term of the Day. Best of Techopedia weekly. News and Special Offers occasional. Data Matching. Techopedia Explains Data Matching. What Does Data Matching Mean? Further, there is concern about the storage of large amounts of personal information gathered for the purpose of data-matching or data-mining.
This information is often generated by individuals conducting everyday activities, such as withdrawing cash from ATMs; paying with debit or credit cards; using loyalty cards; borrowing money; writing cheques; renting a car or a video; making a telephone call or an insurance claim; and, increasingly, sending or receiving e-mail and surfing the Net.
Stay informed with all of the latest news from the ALRC. For example, it is a way to avoid duplicate content. Data matching is also useful in different kinds of data mining. Data matching can also serve the purpose of identifying links between two data sets. The applications for data matching and database matching are numerous. Below are a few examples:. When handling large amounts of data, data matching allows you to perform more precise and accurate searches and analyze data at a more advanced level and with more reliable results.
Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases.
Finally, it briefly describes the main features of many research and open source systems available today. By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching.
To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization.
Such practical considerations are discussed for each of the major steps in the data matching process. Skip to main content Skip to table of contents.
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