By Peter Christen
Data matching (also often called list or info linkage, entity solution, item identity, or box matching) is the duty of deciding on, matching and merging files that correspond to an identical entities from a number of databases or perhaps inside one database. in response to examine in a variety of domain names together with utilized facts, overall healthiness informatics, info mining, computer studying, man made intelligence, database administration, and electronic libraries, major advances were completed over the past decade in all features of the knowledge matching technique, specially on the right way to increase the accuracy of knowledge matching, and its scalability to massive databases.
Peter Christen’s ebook is split into 3 components: half I, “Overview”, introduces the topic via proposing a number of pattern functions and their specific demanding situations, in addition to a normal evaluate of a well-known info matching procedure. half II, “Steps of the information Matching Process”, then info its major steps like pre-processing, indexing, box and list comparability, category, and caliber assessment. finally, half III, “Further Topics”, bargains with particular features like privateness, real-time matching, or matching unstructured information. ultimately, it in brief describes the most positive aspects of many study and open resource structures to be had today.
By offering the reader with a extensive diversity of information matching strategies and methods and concerning all elements of the knowledge matching strategy, this ebook is helping researchers in addition to scholars focusing on info caliber or information matching features to familiarize themselves with contemporary examine advances and to spot open examine demanding situations within the sector of information matching. To this finish, every one bankruptcy of the e-book contains a ultimate part that offers tips that could extra historical past and study fabric. Practitioners will greater comprehend the present state-of-the-art in info matching in addition to the inner workings and boundaries of present platforms. specifically, they're going to examine that it's always now not possible to easily enforce an latest off-the-shelf information matching process with out enormous adaption and customization. Such functional concerns are mentioned for every of the foremost steps within the facts matching process.
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Extra resources for Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection
However, because people can move, change their names, or might not even be registered (for example in a telephone directory), such name verification and correction might not help much to improve data quality. Rather, it might lead to wrong ‘corrections’ being introduced. It is also possible, as illustrated in the pre-processed database tables in Fig. 3, to add attributes that are derived from existing attributes. For example, the gender of a person can often be correctly established from their given name (if a given name is distinctively used for males or females only).
Several records that refer to the same entity), then the maximum number of true matches that are possible is always smaller than or equal to the number of records in the smaller of the two databases. To reduce the possibly very large number of pairs of records that need to be compared, indexing techniques are commonly applied . These techniques filter out record pairs that are very unlikely to correspond to matches. They generate candidate record pairs that will be compared in more detail in the comparison step of the data matching process to calculate the detailed similarities between two records, as will be described in the following section.
Rather, it might lead to wrong ‘corrections’ being introduced. It is also possible, as illustrated in the pre-processed database tables in Fig. 3, to add attributes that are derived from existing attributes. For example, the gender of a person can often be correctly established from their given name (if a given name is distinctively used for males or females only). Similarly, if a postcode (or zipcode) value is missing in a record, its value could be extracted from the corresponding suburb or town name in case there is a unique postcode and suburb name combination.