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Entity Resolution and Information Quality

Entity Resolution and Information Quality
A Book

by John R. Talburt

  • Publisher : Elsevier
  • Release : 2011-01-14
  • Pages : 256
  • ISBN : 9780123819734
  • Language : En, Es, Fr & De
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Entity Resolution and Information Quality presents topics and definitions, and clarifies confusing terminologies regarding entity resolution and information quality. It takes a very wide view of IQ, including its six-domain framework and the skills formed by the International Association for Information and Data Quality {IAIDQ). The book includes chapters that cover the principles of entity resolution and the principles of Information Quality, in addition to their concepts and terminology. It also discusses the Fellegi-Sunter theory of record linkage, the Stanford Entity Resolution Framework, and the Algebraic Model for Entity Resolution, which are the major theoretical models that support Entity Resolution. In relation to this, the book briefly discusses entity-based data integration (EBDI) and its model, which serve as an extension of the Algebraic Model for Entity Resolution. There is also an explanation of how the three commercial ER systems operate and a description of the non-commercial open-source system known as OYSTER. The book concludes by discussing trends in entity resolution research and practice. Students taking IT courses and IT professionals will find this book invaluable. First authoritative reference explaining entity resolution and how to use it effectively Provides practical system design advice to help you get a competitive advantage Includes a companion site with synthetic customer data for applicatory exercises, and access to a Java-based Entity Resolution program.

Data Matching

Data Matching
Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection

by Peter Christen

  • Publisher : Springer Science & Business Media
  • Release : 2012-07-04
  • Pages : 272
  • ISBN : 3642311644
  • Language : En, Es, Fr & De
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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.

Innovative Techniques and Applications of Entity Resolution

Innovative Techniques and Applications of Entity Resolution
A Book

by Wang, Hongzhi

  • Publisher : IGI Global
  • Release : 2014-02-28
  • Pages : 398
  • ISBN : 1466651997
  • Language : En, Es, Fr & De
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Entity resolution is an essential tool in processing and analyzing data in order to draw precise conclusions from the information being presented. Further research in entity resolution is necessary to help promote information quality and improved data reporting in multidisciplinary fields requiring accurate data representation. Innovative Techniques and Applications of Entity Resolution draws upon interdisciplinary research on tools, techniques, and applications of entity resolution. This research work provides a detailed analysis of entity resolution applied to various types of data as well as appropriate techniques and applications and is appropriately designed for students, researchers, information professionals, and system developers.

Information Quality and Governance for Business Intelligence

Information Quality and Governance for Business Intelligence
A Book

by Yeoh, William

  • Publisher : IGI Global
  • Release : 2013-12-31
  • Pages : 478
  • ISBN : 1466648937
  • Language : En, Es, Fr & De
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Business intelligence initiatives have been dominating the technology priority list of many organizations. However, the lack of effective information quality and governance strategies and policies has been meeting these initiatives with some challenges. Information Quality and Governance for Business Intelligence presents the latest exchange of academic research on all aspects of practicing and managing information using a multidisciplinary approach that examines its quality for organizational growth. This book is an essential reference tool for researchers, practitioners, and university students specializing in business intelligence, information quality, and information systems.

Information Quality in Information Fusion and Decision Making

Information Quality in Information Fusion and Decision Making
A Book

by Éloi Bossé,Galina L. Rogova

  • Publisher : Springer
  • Release : 2019-04-02
  • Pages : 620
  • ISBN : 303003643X
  • Language : En, Es, Fr & De
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This book presents a contemporary view of the role of information quality in information fusion and decision making, and provides a formal foundation and the implementation strategies required for dealing with insufficient information quality in building fusion systems for decision making. Information fusion is the process of gathering, processing, and combining large amounts of information from multiple and diverse sources, including physical sensors to human intelligence reports and social media. That data and information may be unreliable, of low fidelity, insufficient resolution, contradictory, fake and/or redundant. Sources may provide unverified reports obtained from other sources resulting in correlations and biases. The success of the fusion processing depends on how well knowledge produced by the processing chain represents reality, which in turn depends on how adequate data are, how good and adequate are the models used, and how accurate, appropriate or applicable prior and contextual knowledge is. By offering contributions by leading experts, this book provides an unparalleled understanding of the problem of information quality in information fusion and decision-making for researchers and professionals in the field.

Entity Information Life Cycle for Big Data

Entity Information Life Cycle for Big Data
Master Data Management and Information Integration

by John R. Talburt,Yinle Zhou

  • Publisher : Morgan Kaufmann
  • Release : 2015-04-20
  • Pages : 254
  • ISBN : 012800665X
  • Language : En, Es, Fr & De
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Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data’s impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics. Explains the business value and impact of entity information management system (EIMS) and directly addresses the problem of EIMS design and operation, a critical issue organizations face when implementing MDM systems Offers practical guidance to help you design and build an EIM system that will successfully handle big data Details how to measure and evaluate entity integrity in MDM systems and explains the principles and processes that comprise EIM Provides an understanding of features and functions an EIM system should have that will assist in evaluating commercial EIM systems Includes chapter review questions, exercises, tips, and free downloads of demonstrations that use the OYSTER open source EIM system Executable code (Java .jar files), control scripts, and synthetic input data illustrate various aspects of CSRUD life cycle such as identity capture, identity update, and assertions

Information Quality Management

Information Quality Management
Theory and Applications

by Latif Al-Hakim

  • Publisher : IGI Global
  • Release : 2007-01-01
  • Pages : 301
  • ISBN : 1599040247
  • Language : En, Es, Fr & De
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Technologies such as the Internet and mobile commerce bring with them ubiquitous connectivity, real-time access, and overwhelming volumes of data and information. The growth of data warehouses and communication and information technologies has increased the need for high information quality management in organizations. Information Quality Management: Theory and Applications provides solutions to information quality problems becoming increasingly prevalent.Information Quality Management: Theory and Applications provides insights and support for professionals and researchers working in the field of information and knowledge management, information quality, practitioners and managers of manufacturing, and service industries concerned with the management of information.

Entity Resolution in the Web of Data

Entity Resolution in the Web of Data
A Book

by Vassilis Christophides,Vasilis Efthymiou,Kostas Stefanidis

  • Publisher : Morgan & Claypool Publishers
  • Release : 2015-08-01
  • Pages : 122
  • ISBN : 1627058044
  • Language : En, Es, Fr & De
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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.

Handbook of Data Quality

Handbook of Data Quality
Research and Practice

by Shazia Sadiq

  • Publisher : Springer Science & Business Media
  • Release : 2013-08-13
  • Pages : 438
  • ISBN : 3642362575
  • Language : En, Es, Fr & De
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The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accuracy, currency or completeness can have erroneous and even catastrophic results. With these changes, traditional approaches to data management in general, and data quality control specifically, are challenged. There is an evident need to incorporate data quality considerations into the whole data cycle, encompassing managerial/governance as well as technical aspects. Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches. Accordingly, Sadiq structured this handbook in four parts: Part I is on organizational solutions, i.e. the development of data quality objectives for the organization, and the development of strategies to establish roles, processes, policies, and standards required to manage and ensure data quality. Part II, on architectural solutions, covers the technology landscape required to deploy developed data quality management processes, standards and policies. Part III, on computational solutions, presents effective and efficient tools and techniques related to record linkage, lineage and provenance, data uncertainty, and advanced integrity constraints. Finally, Part IV is devoted to case studies of successful data quality initiatives that highlight the various aspects of data quality in action. The individual chapters present both an overview of the respective topic in terms of historical research and/or practice and state of the art, as well as specific techniques, methodologies and frameworks developed by the individual contributors. Researchers and students of computer science, information systems, or business management as well as data professionals and practitioners will benefit most from this handbook by not only focusing on the various sections relevant to their research area or particular practical work, but by also studying chapters that they may initially consider not to be directly relevant to them, as there they will learn about new perspectives and approaches.

Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications

Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Concepts, Methodologies, Tools, and Applications

by Wang, John

  • Publisher : IGI Global
  • Release : 2008-05-31
  • Pages : 4092
  • ISBN : 159904952X
  • Language : En, Es, Fr & De
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In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.

Databases Theory and Applications

Databases Theory and Applications
25th Australasian Database Conference, ADC 2014, Brisbane, QLD, Australia, July 14-16, 2014. Proceedings

by Hua Wang,Mohamed A. Sharaf

  • Publisher : Springer
  • Release : 2014-07-04
  • Pages : 231
  • ISBN : 3319086081
  • Language : En, Es, Fr & De
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This book constitutes the refereed proceedings of the 25th Australasian Database Conference, ADC 2014, held in Brisbane, NSW, Australia, in July 2014. The 15 full papers presented together with 6 short papers and 2 keynotes were carefully reviewed and selected from 38 submissions. A large variety of subjects are covered, including hot topics such as data warehousing; database integration; mobile databases; cloud, distributed, and parallel databases; high dimensional and temporal data; image/video retrieval and databases; database performance and tuning; privacy and security in databases; query processing and optimization; semi-structured data and XML; spatial data processing and management; stream and sensor data management; uncertain and probabilistic databases; web databases; graph databases; web service management; and social media data management.

Foundations of Data Quality Management

Foundations of Data Quality Management
A Book

by Wenfei Fan,Floris Geerts

  • Publisher : Morgan & Claypool Publishers
  • Release : 2012-08-15
  • Pages : 217
  • ISBN : 1608457788
  • Language : En, Es, Fr & De
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Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules. The text is organized into seven chapters, focusing on relational data. Chapter One introduces data quality issues. A conditional dependency theory is developed in Chapter Two, for capturing data inconsistencies. It is followed by practical techniques in Chapter 2b for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter Three, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter Four, revising the classical Closed World Assumption and the Open World Assumption, to characterize incomplete information in the real world. A data currency model is presented in Chapter Five, to identify the current values of entities in a database and to answer queries with the current values, in the absence of reliable timestamps. Finally, interactions between these data quality issues are explored in Chapter Six. Important theoretical results and practical algorithms are covered, but formal proofs are omitted. The bibliographical notes contain pointers to papers in which the results were presented and proven, as well as references to materials for further reading. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of data quality. The fundamental research on data quality draws on several areas, including mathematical logic, computational complexity and database theory. It has raised as many questions as it has answered, and is a rich source of questions and vitality. Table of Contents: Data Quality: An Overview / Conditional Dependencies / Cleaning Data with Conditional Dependencies / Data Deduplication / Information Completeness / Data Currency / Interactions between Data Quality Issues

Mining Social Networks and Security Informatics

Mining Social Networks and Security Informatics
A Book

by Tansel Özyer,Zeki Erdem,Jon Rokne,Suheil Khoury

  • Publisher : Springer Science & Business Media
  • Release : 2013-06-01
  • Pages : 283
  • ISBN : 940076359X
  • Language : En, Es, Fr & De
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Crime, terrorism and security are in the forefront of current societal concerns. This edited volume presents research based on social network techniques showing how data from crime and terror networks can be analyzed and how information can be extracted. The topics covered include crime data mining and visualization; organized crime detection; crime network visualization; computational criminology; aspects of terror network analyses and threat prediction including cyberterrorism and the related area of dark web; privacy issues in social networks; security informatics; graph algorithms for social networks; general aspects of social networks such as pattern and anomaly detection; community discovery; link analysis and spatio-temporal network mining. These topics will be of interest to researchers and practitioners in the general area of security informatics. The volume will also serve as a general reference for readers that would want to become familiar with current research in the fast growing field of cybersecurity.

Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics

Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics
Concepts and Competitive Analytics

by Taniar, David

  • Publisher : IGI Global
  • Release : 2009-02-28
  • Pages : 390
  • ISBN : 160566233X
  • Language : En, Es, Fr & De
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Provides developments and research, as well as current innovative activities in data warehousing and mining, focusing on the intersection of data warehousing and business intelligence.

Proceedings of ... ACM/IEEE-CS Joint Conference on Digital Libraries

Proceedings of ... ACM/IEEE-CS Joint Conference on Digital Libraries
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2007
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings

by Takashi Washio,Einoshin Suzuki,Kai Ming Ting,Akihiro Inokuchi

  • Publisher : Springer Science & Business Media
  • Release : 2008-05-08
  • Pages : 1102
  • ISBN : 3540681248
  • Language : En, Es, Fr & De
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This book constitutes the refereed proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008, held in Osaka, Japan, in May 2008. The 37 revised long papers, 40 revised full papers, and 36 revised short papers presented together with 1 keynote talk and 4 invited lectures were carefully reviewed and selected from 312 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scientific discovery, data visualization, causal induction, and knowledge-based systems.

Opening Information Horizons

Opening Information Horizons
6th ACM/IEEE-CS Joint Conference on Digital Libraries

by Anonim

  • Publisher : IEEE
  • Release : 2006
  • Pages : 386
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Information Quality Management

Information Quality Management
A Book

by Guy V. Tozer

  • Publisher : Wiley-Blackwell
  • Release : 1994
  • Pages : 172
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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As business enterprises become ever more dependent in their decision making upon the quality of the data being gathered and processed by information systems, so there are increasing concerns over the ability of the data providers to sustain a consistent level of quality.

Healthcare Business Intelligence

Healthcare Business Intelligence
A Guide to Empowering Successful Data Reporting and Analytics

by Laura Madsen

  • Publisher : John Wiley & Sons
  • Release : 2012-07-20
  • Pages : 336
  • ISBN : 1118282337
  • Language : En, Es, Fr & De
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Solid business intelligence guidance uniquely designed for healthcare organizations Increasing regulatory pressures on healthcare organizations have created a national conversation on data, reporting and analytics in healthcare. Behind the scenes, business intelligence (BI) and data warehousing (DW) capabilities are key drivers that empower these functions. Healthcare Business Intelligence is designed as a guidebook for healthcare organizations dipping their toes into the areas of business intelligence and data warehousing. This volume is essential in how a BI capability can ease the increasing regulatory reporting pressures on all healthcare organizations. Explores the five tenets of healthcare business intelligence Offers tips for creating a BI team Identifies what healthcare organizations should focus on first Shows you how to gain support for your BI program Provides tools and techniques that will jump start your BI Program Explains how to market and maintain your BI Program The risk associated with doing BI/DW wrong is high, and failures are well documented. Healthcare Business Intelligence helps you get it right, with expert guidance on getting your BI program started and successfully keep it going.

Beyond Big Data

Beyond Big Data
Using Social MDM to Drive Deep Customer Insight

by Martin Oberhofer,Eberhard Hechler,Ivan Milman,Scott Schumacher,Dan Wolfson

  • Publisher : IBM Press
  • Release : 2014-10-17
  • Pages : 272
  • ISBN : 0133509818
  • Language : En, Es, Fr & De
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Drive Powerful Business Value by Extending MDM to Social, Mobile, Local, and Transactional Data Enterprises have long relied on Master Data Management (MDM) to improve customer-related processes. But MDM was designed primarily for structured data. Today, crucial information is increasingly captured in unstructured, transactional, and social formats: from tweets and Facebook posts to call center transcripts. Even with tools like Hadoop, extracting usable insight is difficult—often, because it’s so difficult to integrate new and legacy data sources. In Beyond Big Data, five of IBM’s leading data management experts introduce powerful new ways to integrate social, mobile, location, and traditional data. Drawing on pioneering experience with IBM’s enterprise customers, they show how Social MDM can help you deepen relationships, improve prospect targeting, and fully engage customers through mobile channels. Business leaders and practitioners will discover powerful new ways to combine social and master data to improve performance and uncover new opportunities. Architects and other technical leaders will find a complete reference architecture, in-depth coverage of relevant technologies and use cases, and domain-specific best practices for their own projects. Coverage Includes How Social MDM extends fundamental MDM concepts and techniques Architecting Social MDM: components, functions, layers, and interactions Identifying high value relationships: person to product and person to organization Mapping Social MDM architecture to specific products and technologies Using Social MDM to create more compelling customer experiences Accelerating your transition to highly-targeted, contextual marketing Incorporating mobile data to improve employee productivity Avoiding privacy and ethical pitfalls throughout your ecosystem Previewing Semantic MDM and other emerging trends