By Peter Christen
Data matching (also referred to as checklist or facts linkage, entity solution, item identity, or box matching) is the duty of choosing, matching and merging documents that correspond to an identical entities from numerous databases or perhaps inside one database. according to study in a variety of domain names together with utilized information, health and wellbeing informatics, info mining, laptop studying, man made intelligence, database administration, and electronic libraries, major advances were accomplished during the last decade in all points of the knowledge matching approach, in particular on the 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 by way of providing numerous pattern purposes and their distinctive demanding situations, in addition to a basic review of a usual info matching approach. half II, “Steps of the information Matching Process”, then info its major steps like pre-processing, indexing, box and checklist comparability, class, and caliber evaluate. finally, half III, “Further Topics”, offers with particular features like privateness, real-time matching, or matching unstructured info. ultimately, it in brief describes the most beneficial properties of many examine and open resource platforms to be had today.
By delivering the reader with a extensive variety of information matching suggestions and methods and pertaining to all points of the knowledge matching procedure, this ebook is helping researchers in addition to scholars focusing on info caliber or information matching points to familiarize themselves with contemporary learn advances and to spot open examine demanding situations within the region of information matching. To this finish, every one bankruptcy of the booklet incorporates a ultimate part that offers tips to additional historical past and study fabric. Practitioners will larger comprehend the present state-of-the-art in facts matching in addition to the inner workings and boundaries of present structures. particularly, they are going to study that it's always now not possible to easily enforce an current off-the-shelf facts matching approach with no gigantic adaption and customization. Such sensible concerns are mentioned for every of the main steps within the facts matching process.
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Extra info for Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection
This step deals with the common situation of database attributes that contain several pieces of information, such as the ‘Address’ attribute of the second database in Fig. 2. Finding a match between the content of this attribute and the content of the corresponding set of attributes in the first database (‘Street’, ‘Suburb’, ‘Postcode’ and ‘State’) is challenging. It is of advantage for data matching to split the content of attributes that contain several pieces of information into a set of new attributes that each contain one well-defined piece of information.
This root condition is highly relevant when databases that contain personal information are to be matched across organisations. 1 Data Quality Issues Relevant to Data Matching • • • • • • 41 projects. The topic of privacy within the context of data matching will be covered in detail in Chap. 8. Coded data across disciplines. This condition will affect the consistency of data between different databases. If the databases to be matched originate in different organisations or different disciplines, then careful mapping between different formats and encodings is required before any matching can be attempted.
8. Coded data across disciplines. This condition will affect the consistency of data between different databases. If the databases to be matched originate in different organisations or different disciplines, then careful mapping between different formats and encodings is required before any matching can be attempted. Complex data representations. Many traditional data matching algorithms can only be applied on data that are made of strings (such as name and address values) or numerical values (such as dates or age values).