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计算机专业英语期末作业

计算机专业英语期末作业
计算机专业英语期末作业

第一篇参考文献

AN APPLICATION OF THE FELLEGI-SUNTER MODEL OF RECORD LINKAGE TO THE 1990 U.S. DECENNIAL

CENSUS

记录连接的FELLEGI-SUNTER模型在美国1990年十年人口普查中的应用

William E. Winkler and Yves Thibaudeau

U.S. Bureau of the Census

Abstract:This paper describes a methodology for computer matching the Post Enumeration Survey with the Census. Computer matching is the first stage of a process for producing adjusted Census counts. All crucial matching parameters are computed solely using characteristics of the files being matched. No a priori knowledge of truth of matches is assumed. No previously created lookup tables are needed. The methods are illustrated with numerical results using files from the 1988 Dress Rehearsal Census for which the truth of matches is known.

Key words and phrases : EM Algorithm ; String Comparator Metric ; LP Algorithm; Decision Rule ; Error Rate.

摘要:本文介绍了一种电脑匹配人口普查的覆核统计调查的方法。电脑匹配是产生调整后人口普查计数的第一阶段。所有关键的匹配参数计算仅使用被匹配的文件的特点。没有假定的先验匹配真理的知识。先前创建的查找表也不是必要的。这个方法说明了使用了已知匹配的真相的1988年的预演人口普查的档案的计算结果。

关键词和短语:EM算法;字符串比较公制; LP算法;决策规则;错误率。

第二篇参考文献

Data Cleaning: Problems and Current Approaches

数据清洗:问题与目前的做法

Erhard Rahm , Hong Hai Do

University of Leipzig, Germany

Abstract:We classify data quality problems that are addressed by data cleaning and provide an overview of the main solution approaches. Data cleaning is especially required when integrating heterogeneous data sources and should be addressed together with schema-related data transformations. In data warehouses, data cleaning is a major part of the so-called ETL process. We also discuss current tool support for data cleaning.

摘要:我们给处理数据清洗的数据质量问题分类,并提供了一个概览的主要解决办法。数据清洗是必要的,尤其是集成异构数据源时,应与模式相关的数据转换一同处理。在数据仓库,数据清洗是一个所谓的ETL过程中的重要组成部分。

我们还讨论了当前数据清洗工具的支持。

第三篇参考文献

Record Linkage: Current Practice and Future

记录链接:目前的实践和未来

Directions

Lifang Gu, Rohan Baxter, Deanne Vickers, and Chris Rainsford

CSIRO Mathematical and Information Sciences

GPO Box 664, Canberra, ACT 2601, Australia

CMIS Technical Report No. 03/83

A bstract: Record linkage is the task of quickly and accurately identifying records corresponding to the same entity from one or more data sources. Record linkage is also known as data cleaning, entity reconciliation or identification and the merge/purge problem. This paper presents the “standard” probabilistic record linkage model and the associated algorithm. Recent work in information retrieval, federated database systems and data mining have proposed alternatives to key components of the standard algorithm. The impact of these alternatives on the standard approach are assessed. The key question is whether and how these new alternatives are better in terms of time, accuracy and degree of automation for a particular record linkage application.

Keywords: record linkage, data cleaning, entity identification, entity reconciliation, object isomerism, merge/purge, list washing.

摘要

记录链接是迅速和准确地识别对应于同一个实体从一个或多个数据源的记录的工作。记录链接也被称为数据清洗,实体核对或鉴定,并合并/净化问题。本文提出了“标准”的概率纪录链接模型及相关算法。最近的在信息检索方面的工作,联合数据库系统和数据挖掘提出了标准算法的关键部件的替代物。对这些替代品的标准方法的影响进行评估。关键的问题是,这些新的替代品是否以及如何将在一个特定的记录链接应用更省时间,并获得更高的准确性和自动化程度。

关键字:记录联动,数据清洗,实体识别,实体核对,异构对象,合并/净化,清洗列表。

第四篇参考文献

LEARNING OBJECT IDENTIFICATION RULES FOR

INFORMATION INTEGRATION

信息集成学习对象识别规则

Sheila Tejada1, Craig A. Knoblock1, and Steven Minton2 1University of Southern California/Information Sciences Institute, 4676 Admiralty Way,

Marina del Rey CA 90292

2Fetch Technologies 4676 Admiralty Way, Marina del Rey CA 90292

(December 2001)

Abstract:When integrating information from multiple websites, the same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match. We have developed an object identification system called Active Atlas, which compares the objects' shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist between two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings between objects. This manual process is time consuming and error-prone. In our approach, Active Atlas learns to tailor mapping rules, through limited user input, to a specific application domain. The experimental results demonstrate that we achieve higher accuracy and require less user involvement than previous methods across various application domains.

摘要:整合来自多个网站的信息时,相同的数据对象可能存在跨站点的文本格式不一致,因此很难确定使用精确的文本匹配的匹配对象。我们已经开发了一个称为Active Atlas的对象识别系统,它比较对象的共同属性,以确定匹配的对象。决定是否应该存在两个对象之间的映射时,特定的属性更重要。以前的对象识别方法都需要对象识别规则或确定对象之间映射的映射规则的人工构建。这个手动的过程非常耗时而且容易出错。在我们的方法中,Active Atlas主动学习度身订造的映射规则,通过有限的用户输入,应用到一个特定的应用领域。实验结果表明,我们实现了更高的准确性和在各个应用领域要求比以前的方法更少的用户参与。

第五篇参考文献

Evaluating String Comparator Performance for Record Linkage 字符串记录链接的性能比较评价

William E. Yancey

Statistical Research Division

U.S. Census Bureau

June 9, 2005

Abstract:We compare variations of string comparators based on the Jaro-Winkler comparator and edit distance comparator. We apply the comparators to Census data to see which are better classifiers for matches and non-matches, first by comparing their classification abilities using a ROC curve based analysis, then by considering a direct comparison between two candidate comparators in record linkage results.

摘要:我们在Jaro-Winkler比较和修改距离比较器的基础上比较字符串的变化。我们运用人口普查数据的比较来查看更好的匹配和非匹配分类器,首先通过采用

基于ROC曲线分析,然后研究两个候选比较器在记录链接结果的直接对比,来比较其分类能力。

Introduction

第一段英文原稿

We wish to evaluate the performance of some string comparators and variations for use in record linkage software for Census Bureau data. For record linkage, under the conditional independence assumption, we compute a comparison weight for two records from the sum of the comparison weights of the individual matching fields. If we designate that the values of a matching field agree for two records by γ= 1 and that they disagree by γ= 0, then we define the agreement weight for the two fields by

aw = Pr (γ= 1|M)/ Pr (γ= 1|U )

and the disagreement weight by

dw = Pr (γ= 0|M) /Pr (γ= 0|U )

where the probabilities are conditioned by whether the two records do in fact belong to the set M of true matches or the set U of true non-matches. If we wish to use a string comparator for the matching field with alphabet Σ, we generally use a similarity function

γ: Σ?× Σ?→ [0, 1]

where γ(α, β) = 1 when the strings α, βare identical. We then use an interpolation function w,

w : [0, 1] → [dw, aw]

to assign a comparison weight w (x) to a pair of strings α, βwhere the interpolation function is increasing with w (1) = aw.

翻译:我们希望评估一些字符串比较和变化在人口普查局的数据记录链接软件的使用的性能。对记录链接来说,条件独立性假设下,我们从个体的匹配字段比较重的总和计算两个记录比较重。如果我们指定两个记录匹配字段的值一致为γ= 1,不一致为γ= 0,然后,我们定义这两个字段的一致的权重为

aw = Pr (γ= 1|M)/ Pr (γ= 1|U )

不一致的权重为

dw = Pr (γ= 0|M) /Pr (γ= 0|U )

概率的条件是两个记录是否确实属于实际上准确的匹配设置的M或准确的非匹配的集U。如果我们想要使用字母表Σ匹配字段的字符串比较,我们通常使用一个相似的函数

γ: Σ?× Σ?→ [0, 1]

其中,当字符串α,β相同时,γ(α, β) = 1,。然后,我们用一个插值函数w

w : [0, 1] → [dw, aw]

来分析比较当插值函数以W(1)= AW增加时,一对字符串的α,β的权重W (x)。

第二段英文原稿

We next describe the string comparator functions that we used for this study. We then discuss the data sets that were used to test the comparators. Then we discuss how we interpreted the results of this data to try to evaluate the classification power of each of the string comparators. We also look at the difference that the string comparator choice can make in a matching situation.

翻译:接下来我们描述,我们这项研究中使用的字符串比较函数。然后,我们讨论被用来测试比较的数据集。然后我们讨论如何解释这些数据的结果,并设法评估每个字符串比较的分类能力。我们也查找在匹配的情况中字符串比较器可能的选择的区别。

论文清单

[1] William E. Winkler and Yves Thibaudeau .AN APPLICATION OF THE FELLEGI-SUNTER MODEL OF RECORD LINKAGE TO THE 1990 U.S. DECENNIAL CENSUS.U.S. Bureau of the Census.

[2] Erhard Rahm* ,Hong Hai Do. Data Cleaning: Problems and Current Approaches. University of Leipzig, Germany.

[3] Lifang Gu , Rohan Baxter, Deanne Vickers, and Chris Rainsford .Record Linkage: Current Practice and Future Directions .CSIRO Mathematical and Information Sciences GPO Box 664, Canberra, ACT 2601, Australia CMIS Technical Report No. 03/83.

[4] Sheila Tejada1, Craig A. Knoblock1, and Steven Minton2.LEARNING OBJECT IDENTIFICATION RULESFOR INFORMATION INTEGRATION.1University of Southern California/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey CA 90292.2Fetch Technologies 4676 Admiralty Way, Marina del Rey CA 90292 (December 2001).

[5] William E. Yancey. Evaluating String Comparator Performance for Record Linkage . Statistical Research Division .U.S . Census Bureau June 9, 2005

[6] William E Winkler. DataClening Methods. Conference SIGKDD ’03,Auguest

24-27,2003 Washington,DC

[7] Y. Richard Wang and Stuart E. Madnick . The inter-database instance identication problem in integrating autonomous systems. In Proceedings of the Fifth IEEE International Conference on Data Engineering (ICDE 1989), pages

46.55,1989.

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