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Titlebook: An Introduction to Duplicate Detection; Felix Naumann,Melanie Herschel Book 2010 Springer Nature Switzerland AG 2010

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發(fā)表于 2025-3-21 17:28:32 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱An Introduction to Duplicate Detection
影響因子2023Felix Naumann,Melanie Herschel
視頻videohttp://file.papertrans.cn/156/155223/155223.mp4
學(xué)科分類Synthesis Lectures on Data Management
圖書(shū)封面Titlebook: An Introduction to Duplicate Detection;  Felix Naumann,Melanie Herschel Book 2010 Springer Nature Switzerland AG 2010
影響因子With the ever increasing volume of data, data quality problems abound. Multiple, yet different representations of the same real-world objects in data, duplicates, are one of the most intriguing data quality problems. The effects of such duplicates are detrimental; for instance, bank customers can obtain duplicate identities, inventory levels are monitored incorrectly, catalogs are mailed multiple times to the same household, etc. Automatically detecting duplicates is difficult: First, duplicate representations are usually not identical but slightly differ in their values. Second, in principle all pairs of records should be compared, which is infeasible for large volumes of data. This lecture examines closely the two main components to overcome these difficulties: (i) Similarity measures are used to automatically identify duplicates when comparing two records. Well-chosen similarity measures improve the effectiveness of duplicate detection. (ii) Algorithms are developed to perform on very large volumes of data in search for duplicates. Well-designed algorithms improve the efficiency of duplicate detection. Finally, we discuss methods to evaluate the success of duplicate detection. T
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Book 2010 duplicates, are one of the most intriguing data quality problems. The effects of such duplicates are detrimental; for instance, bank customers can obtain duplicate identities, inventory levels are monitored incorrectly, catalogs are mailed multiple times to the same household, etc. Automatically de
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Das extrapyramidal-motorische System,e real-world object in the data. For instance, an individual might be represented multiple times in a customer database, a single product might be listed many times in an online catalog, and data about a single type protein might be stored in many different scientific databases.
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Problem Definition,ection in data stored in a single relation, a focus we maintain throughout this lecture. We then discuss the complexity of the problem in Section 2.2. Finally, in Section 2.3, we highlight issues and opportunities that exist when data exhibit more complex relationships than a single relation.
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Evaluating Detection Success,nd. Difficulties that prevent a benchmark data set are privacy and confidentiality concerns regarding the data. In this section, we first describe standard measures for success, in particular precision and recall. We then proceed to discuss existing data sets and data generators.
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