- (Elmagarmid et al., 2007) ⇒ Ahmed K. Elmagarmid, Panagiotis G. Ipeirotis, and Vassilios S. Verykio. (2007). “Duplicate Record Detection: A Survey.” In: IEEE Transactions on Knowledge and Data Engineering (1). doi:10.1109/TKDE.2007.9
- It uses the term Duplicate Record Detection for Record Coreference Resolution.
- It defines the Record Coreference Resolution Task as the “detection of duplicate database records.”
- It distinguishes between:
- It is focused on Entity Databases where Data Records refer to "real-world objects" (e.g., StreetAddress=44 W. 4th St. vs. StreetAddress=44 West Fourth Street).
- It does not address the problems of:
- It focuses its analysis on Supervised Record Coreference Resolution Algorithms and Unsupervised Record Coreference Resolution Algorithms.
- It suggests Semi-Supervised Record Coreference Resolution Algorithms as a future research area.
- (Xiao et al., 2011) ⇒ Chuan Xiao, Wei Wang, Xuemin Lin, Jeffrey Xu Yu, and author:Guoren Wang. (2011). “Efficient Similarity Joins for Near-duplicate Detection.” In: ACM Transactions on Database Systems (TODS) Journal, 36(3). doi:10.1145/2000824.2000825
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 a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats or any combination of these factors. In this article, we present a thorough analysis of the literature on duplicate record detection. We cover similarity metrics that are commonly used to detect similar field entries, and we present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database. We also cover multiple techniques for improving the efficiency and scalability of ppapproximate duplicate detection algorithm]]s. We conclude with a coverage of existing tools and with a brief discussion of the big open problems in the area.
Databases play an important role in today's IT based economy. Many industries and systems depend on the accuracy of databases to carry out operations. Therefore, the quality of the information (or the lack thereof) stored in the databases, can have significant cost implications to a system that relies on information to function and conduct business. In an error-free system with perfectly clean data, the construction of a comprehensive view of the data consists of linking ?in relational terms, joining? two or more tables on their key fields. Unfortunately, data often lack a unique, global identifier that would permit such an operation. Furthermore, the data are neither carefully controlled for quality nor defined in a consistent way across different data sources. Thus, data quality is often compromised by many factors, including data entry errors (e.g., Microsft instead of Microsoft), missing integrity constraints (e.g., allowing entries such as EmployeeAge=567), and multiple conventions for recording information (e.g., 44 W. 4th St. vs. 44 West Fourth Street). To make things worse, in independently managed databases not only the values, but the structure, semantics and underlying assumptions about the data may differ as well.
Often, while integrating data from different sources to implement a data warehouse, organizations become aware of potential systematic differences or con icts. Such problems fall under the umbrella-term data heterogeneity . Data cleaning , or data scrubbing , refer to the process of resolving such identification problems in the data. We distinguish between two types of data heterogeneity: structural and lexical. Structural heterogeneity occurs when the fields of the tuples in the database are structured differently in different databases. For example, in one database, the customer address might be recorded in one field named, say, addr, while in another database the same information might be stored in multiple fields such as street, city, state, and zipcode. Lexical heterogeneity occurs when the tuples have identically structured fields across databases, but the data use different representations to refer to the same real-world object (e.g., StreetAddress=44 W. 4th St. vs. StreetAddress=44 West Fourth Street).
In this paper, we focus on the problem of lexical heterogeneity and survey various techniques which have been developed for addressing this problem. We focus on the case where the input is a set of structured and properly segmented records, i.e., we focus mainly on cases of database records. Hence, we do not cover solutions for the various other problems, such that of mirror detection, in which the goal is to detect similar or identical web pages (e.g., see , ). Also, we do not cover solutions for problems such as anaphora resolution , in which the problem is to locate different mentions of the same entity in free text (e.g., that the phrase ?President of the U.S.? refers to the same entity as ?George W. Bush?). We should note that the algorithms developed for mirror detection or for anaphora resolution are often applicable for the task of duplicate detection. Techniques for mirror detection have been used for detection of duplicate database records (see, for example, Section V-A.4) and techniques for anaphora resolution are commonly used as an integral part of deduplication in relations that are extracted from free text using information extraction systems .
The problem that we study has been known for more than five decades as the record linkage or the record matching problem , ?,  in the statistics community. The goal of record matching is to identify records in the same or different databases that refer to the same real-world entity, even if the records are not identical. In slightly ironic fashion, the same problem has multiple names across research communities. In the database community, the problem is described as merge-purge , data deduplication , and instance identification ; in the AI community, the same problem is described as database hardening  and name matching . The names coreference resolution, identity uncertainty, and duplicate detection are also commonly used to refer to the same task. We will use the term duplicate record detection in this paper.
The remaining part of this paper is organized as follows: In Section II, we brie y discuss the necessary steps in the data cleaning process, before the duplicate record detection phase. Then, Section III describes techniques used to match individual fields, and Section IV presents techniques for matching records that contain multiple fields. Section V describes methods for improving the efficiency of the duplicate record detection process and Section VI presents a few commercial, off-the-shelf tools used in industry for duplicate record detection and for evaluating the initial quality of the data and of the matched records. Finally, Section VII concludes the paper and discusses interesting directions for future research.
2. Data Preparation
VII. Future Directions and Conclusion
In this survey, we have presented a comprehensive survey of the existing techniques used for detecting non-identical duplicate entries in database records. The interested reader may also want to read a complementary survey by Winkler  and the Special Issue of the IEEE Data Engineering Bulletin on Data Quality .
As database systems are becoming more and more commonplace, data cleaning is going to be the cornerstone for correcting errors in systems which are accumulating vast amounts of errors on a daily basis. Despite the breadth and depth of the presented techniques, we believe that there is still room for substantial improvements in the current state-of-the-art.
First of all, it is currently unclear which metrics and techniques are the current state-of-the-art. The lack of standardized, large scale benchmarking data sets can be a big obstacle for the further development of the field, as it is almost impossible to convincingly compare new techniques with existing ones. A repository of benchmark data sources with known and diverse characteristics should be made available to developers so they may evaluate their methods during the development process. Along with benchmark and evaluation data, various systems need some form of training data to produce the initial matching model. Although small data sets are available, we are not aware of large-scale, validated data sets that could be used as benchmarks. Winkler  highlights techniques on how to derive data sets that are properly anonymized and are still useful for duplicate record detection purposes. * Currently, there are two main approaches for duplicate record detection. Research in databases emphasizes relatively simple and fast duplicate detection techniques, that can be applied to databases with millions of records. Such techniques typically do not rely on the existence of training data, and emphasize efficiency over effectiveness. On the other hand, research in machine learning and statistics aims to develop more sophisticated matching techniques that rely on probabilistic models. An interesting direction for future research is to develop techniques that combine the best of both worlds.
Most of the duplicate detection systems available today offer various algorithmic approaches for speeding up the duplicate detection process. The changing nature of the duplicate detection process also requires adaptive methods that detect different patterns for duplicate detection and automatically adapt themselves over time. For example, a background process could monitor the current data, incoming data and any data sources that need to be merged or matched, and decide, based on the observed errors, whether a revision of the duplicate detection process is necessary or not. Another related aspect of this challenge is to develop methods that permit the user to derive the proportions of errors expected in data cleaning projects. Finally, large amounts of structured information is now derived from unstructured text and from the web. This information is typically imprecise and noisy; duplicate record detection techniques are crucial for improving the quality of the extracted data. The increasing popularity of information extraction techniques is going to make this issue more prevalent in the future, highlighting the need to develop robust and scalable solutions. This only adds to the sentiment that more research is needed in the area of duplicate record detection and in the area of data cleaning and information quality in general.
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|Author||Ahmed K. Elmagarmid +, Panagiotis G. Ipeirotis + and Vassilios S. Verykio +|
|journal||IEEE Transactions on Knowledge and Data Engineering +|
|title||Duplicate Record Detection: A Survey +|