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Innovative Techniques and Applications of Entity Resolution [Kietas viršelis]

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Pastovi nuoroda: https://www.kriso.lt/db/9781466651982.html
Raktažodžiai:
"This book draws upon interdisciplinary research on tools, techniques, and applications of entity resolution and provides a detailed analysis of entity resolution applied to various types of data as well as appropriate techniques and applications"--

Wang summarizes the current techniques of entity resolution to provide a reference for researchers in databases, data quality, information systems, and information integration. The book can also serve as a textbook for students of such fields as computer science, information systems, and management. It covers principles of entity resolution, entity resolution on various types of data, database techniques and entity resolution, and applications. The topics include measures of entity resolution results, entity resolution of a single relation, basic data operations for entity resolution, entity resolution in bibliographic information management, and entity resolution in healthcare. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)

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.
Foreword x
Preface xi
Acknowledgment xvii
Section 1 Principles of Entity Resolution
Chapter 1 Overview of Entity Resolution
1(14)
Basic Concepts of Entity Resolution
1(2)
Central Issues of Entity Resolution
3(1)
Applications for Entity Resolution
4(1)
Overview for Entity Resolution
5(6)
Future Research Directions
11(1)
Conclusion
12(3)
Chapter 2 Measures of Entity Resolution Result
15(26)
Introduction
15(2)
Background
17(1)
Precision and Recall
17(2)
Distance-Based Measures
19(12)
The Comparisons of Measures
31(4)
Future Research Directions
35(1)
Conclusion
36(5)
Section 2 Entity Resolution on Various Types Of Data
Chapter 3 Entity Resolution on Names
41(26)
Introduction
41(1)
Background
42(1)
Similarity Measure Between Names
43(5)
String Transformation for Entity Resolution
48(5)
Learning Algorithms for Entity Resolutions on Names
53(9)
Future Research Directions
62(1)
Conclusion
63(4)
Chapter 4 Context-Based Entity Resolution
67(20)
Introduction
67(3)
Background
70(3)
Main Focus of the
Chapter
73(1)
Leveraging CED for Entity Resolution
73(6)
Experimental Evaluation
79(4)
Future Research Directions
83(1)
Conclusion
84(3)
Chapter 5 Entity Resolution on Single Relation
87(36)
Introduction
87(1)
Record Similarity Computation
87(3)
Symbols Notation
90(3)
Improvement of the Detecting Technics on Efficacy and the Applications
93(2)
Matching Dependencies and Keys
95(6)
Similarity Threshold Computation
101(8)
Blocking
109(14)
Chapter 6 Entity Resolution on Multiple Relations
123(17)
Introduction
123(1)
Problem Definition
124(3)
Similarity Measure
127(5)
Entity Resolution Algorithm on Multiple Relations
132(5)
Conclusion
137(3)
Chapter 7 XML Object Identification
140(31)
Introduction
140(1)
Background
141(1)
Main Focus of the
Chapter
141(1)
XML Pairwise Entity Resolution
142(26)
Future Research Directions and Conclusion
168(3)
Chapter 8 Entity Resolution on Graph Data Set
171(24)
Introduction
171(1)
Background
172(1)
Distance Definition of Graph
173(8)
Pair-Wise Entity Resolution on Graphs
181(10)
Future Research Directions
191(1)
Conclusion
192(3)
Chapter 9 Entity Resolution on Complex Network
195(27)
Introduction
195(1)
Background
195(1)
The Detection of Mirror Websites (Barabasi & Albert 1999)
196(6)
Name Recognition in Social Network (Fan, Wang, Pu, Zhou & Lv 2011)
202(9)
Description Methods of Node Similarity
211(5)
Future Research Directions
216(1)
Conclusion
216(6)
Chapter 10 Entity Resolution on Cloud
222(15)
Introduction
222(1)
Background
223(1)
An Entity Resolution Method Based on Mapreduce
224(6)
Experimental Results
230(3)
Strategies to Improve Performance
233(1)
Future Research Directions
233(1)
Conclusion
233(4)
Section 3 Database Techniques and Entity Resolution
Chapter 11 Basic Data Operators for Entity Resolution
237(24)
Introduction
237(1)
Similarity Search
238(4)
VGRAM
242(5)
Similarity Join
247(4)
Clustering
251(1)
Center
251(1)
Mergecenter
251(4)
Future Research Directions
255(1)
Conclusion
256(5)
Chapter 12 Data Cleaning Based on Entity Resolution
261(22)
Introduction
261(2)
Background
263(1)
Different Truth Discovery Approaches
264(17)
Summary
281(2)
Chapter 13 Query Processing Based on Entity Resolution
283(56)
Introduction
283(2)
Entity-Based Data Model
285(2)
The Architecture for Entity-Based Databases
287(1)
Query Processing in Entity-Based Databases
288(15)
Query Optimization in Entity-Based Databases
303(28)
Conclusion
331(8)
Section 4 Applications for Entity Resolution
Chapter 14 Duplicate Record Detection for Data Integration
339(20)
Introduction
339(3)
Problem Definition
342(2)
Duplicate Record Detection Based on Similarity Estimation
344(4)
Schema Mapping
348(4)
Experiment Evaluations
352(1)
Conclusion
353(6)
Chapter 15 Entity Resolution in Bibliography Information Management
359(12)
Introduction
359(2)
The Entity Resolution Framework: Eif
361(2)
The Author Resolution Algorithm Based on Eif (Ai-Eif)
363(2)
Experiments
365(4)
Conclusion
369(2)
Chapter 16 Product Entity Resolution in E-Commerce
371(14)
Introduction
371(2)
Related Work
373(1)
System Design
374(7)
Experiment
381(2)
Conclusion
383(2)
Chapter 17 Entity Resolution in Healthcare
385(14)
Introduction
385(2)
Background
387(1)
Abbreviation Disambiguation Systems
388(5)
Evaluation Corpus
393(2)
Conclusion
395(4)
Compilation of References 399(12)
About the Author 411(1)
Index 412
Hongzhi Wang, Ph.D. is an Associate Professor at Massive Data Computing Center in the Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. He received his his BSc, MSc and PHD degrees and computer science from Harbin Institute of Technology in 2001, 2003 and 2008, respectively. His research interest is data and information management in general, particularly in the areas of big data management, data quality, XML and graph data management. He has published more than 100 papers in conferences and journals. He was also awarded Microsoft Fellow, IBM PHD Fellowship and Chinese Excellent Database Engineer. His doctoral dissertation was selected as the outstanding doctoral dissertation by the China Computer Federation.