data matching concepts and techniques for record linkage entity resolution and duplicate detection data centric systems and applications

Download Book Data Matching Concepts And Techniques For Record Linkage Entity Resolution And Duplicate Detection Data Centric Systems And Applications in PDF format. You can Read Online Data Matching Concepts And Techniques For Record Linkage Entity Resolution And Duplicate Detection Data Centric Systems And Applications here in PDF, EPUB, Mobi or Docx formats.

Data Matching

Author : Peter Christen
ISBN : 9783642311642
Genre : Computers
File Size : 82. 68 MB
Format : PDF, ePub
Download : 710
Read : 971

Download Now


Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. 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. Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. 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.

Population Reconstruction

Author : Gerrit Bloothooft
ISBN : 9783319198842
Genre : Social Science
File Size : 36. 61 MB
Format : PDF
Download : 578
Read : 1180

Download Now


This book addresses the problems that are encountered, and solutions that have been proposed, when we aim to identify people and to reconstruct populations under conditions where information is scarce, ambiguous, fuzzy and sometimes erroneous. The process from handwritten registers to a reconstructed digitized population consists of three major phases, reflected in the three main sections of this book. The first phase involves transcribing and digitizing the data while structuring the information in a meaningful and efficient way. In the second phase, records that refer to the same person or group of persons are identified by a process of linkage. In the third and final phase, the information on an individual is combined into a reconstruction of their life course. The studies and examples in this book originate from a range of countries, each with its own cultural and administrative characteristics, and from medieval charters through historical censuses and vital registration, to the modern issue of privacy preservation. Despite the diverse places and times addressed, they all share the study of fundamental issues when it comes to model reasoning for population reconstruction and the possibilities and limitations of information technology to support this process. It is thus not a single discipline that is involved in such an endeavor. Historians, social scientists, and linguists represent the humanities through their knowledge of the complexity of the past, the limitations of sources, and the possible interpretations of information. The availability of big data from digitized archives and the need for complex analyses to identify individuals calls for the involvement of computer scientists. With contributions from all these fields, often in direct cooperation, this book is at the heart of the digital humanities, and will hopefully offer a source of inspiration for future investigations.

Entity Resolution In The Web Of Data

Author : Vassilis Christophides
ISBN : 9781627058049
Genre : Computers
File Size : 37. 90 MB
Format : PDF, Docs
Download : 334
Read : 844

Download Now


In recent years, several knowledge bases have been built to enable large-scale knowledge sharing, but also an entity-centric Web search, mixing both structured data and text querying. These knowledge bases offer machine-readable descriptions of real-world entities, e.g., persons, places, published on the Web as Linked Data. However, due to the different information extraction tools and curation policies employed by knowledge bases, multiple, complementary and sometimes conflicting descriptions of the same real-world entities may be provided. Entity resolution aims to identify different descriptions that refer to the same entity appearing either within or across knowledge bases. The objective of this book is to present the new entity resolution challenges stemming from the openness of the Web of data in describing entities by an unbounded number of knowledge bases, the semantic and structural diversity of the descriptions provided across domains even for the same real-world entities, as well as the autonomy of knowledge bases in terms of adopted processes for creating and curating entity descriptions. The scale, diversity, and graph structuring of entity descriptions in the Web of data essentially challenge how two descriptions can be effectively compared for similarity, but also how resolution algorithms can efficiently avoid examining pairwise all descriptions. The book covers a wide spectrum of entity resolution issues at the Web scale, including basic concepts and data structures, main resolution tasks and workflows, as well as state-of-the-art algorithmic techniques and experimental trade-offs.

Encyclopedia Of Machine Learning And Data Mining Sammut Webb 2nd Ed 2017

Author : Springer Science, Inc
ISBN :
Genre : Computers
File Size : 90. 66 MB
Format : PDF, Mobi
Download : 651
Read : 747

Download Now


Machine learning and data mining are rapidly developing fields. Following the success of the first edition of the Encyclopedia of Machine Learning, we are delighted to bring you this updated and expanded edition. We have expanded the scope, as reflected in the revised title Encyclopedia of Machine Learning and Data Mining, to encompass more of the broader activity that surrounds the machine learning process. This includes new articles in such diverse areas as anomaly detection, online controlled experiments, and record linkage as well as substantial expansion of existing entries such as data preparation. We have also included new entries on key recent developments in core machine learning, such as deep learning. A thorough review has also led to updating of much of the existing content. This substantial tome is the product of an intense effort by many individuals. We thank the Editorial Board and the numerous contributors who have provided the content.We are grateful to the Springer team of Andrew Spencer, Michael Hermann, and Melissa Fearon who have shepherded us through the long process of bringing this second edition to print. We are also grateful to the production staff who have turned the content into its final form. We are confident that this revised encyclopedia will consolidate the first edition’s place as a key reference source for the machine learning and data mining communities.

Intelligent Information And Database Systems

Author : Ngoc Thanh Nguyen
ISBN : 9783319754208
Genre : Computers
File Size : 90. 72 MB
Format : PDF
Download : 803
Read : 546

Download Now


The two-volume set LNAI 10751 and 10752 constitutes the refereed proceedings of the 10th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2018, held in Dong Hoi City, Vietnam, in March 2018. The total of 133 full papers accepted for publication in these proceedings was carefully reviewed and selected from 423 submissions. They were organized in topical sections named: Knowledge Engineering and Semantic Web; Social Networks and Recommender Systems; Text Processing and Information Retrieval; Machine Learning and Data Mining; Decision Support and Control Systems; Computer Vision Techniques; Advanced Data Mining Techniques and Applications; Multiple Model Approach to Machine Learning; Sensor Networks and Internet of Things; Intelligent Information Systems; Data Structures Modeling for Knowledge Representation; Modeling, Storing, and Querying of Graph Data; Data Science and Computational Intelligence; Design Thinking Based R&D, Development Technique, and Project Based Learning; Intelligent and Contextual Systems; Intelligent Systems and Algorithms in Information Sciences; Intelligent Applications of Internet of Thing and Data Analysis Technologies; Intelligent Systems and Methods in Biomedicine; Intelligent Biomarkers of Neurodegenerative Processes in Brain; Analysis of Image, Video and Motion Data in Life Sciences; Computational Imaging and Vision; Computer Vision and Robotics; Intelligent Computer Vision Systems and Applications; Intelligent Systems for Optimization of Logistics and Industrial Applications.

Data Quality

Author : Carlo Batini
ISBN : 9783540331735
Genre : Computers
File Size : 51. 80 MB
Format : PDF, Docs
Download : 280
Read : 899

Download Now


Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone – researchers, students, or professionals – interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.

Top Download:

Best Books