If not, who will be responsible for the data cleaning?
如果不能，谁将负责进行数据清理？
Firstly, the background of data cleaning problem and research status is explained.
介绍数据清洗问题产生的背景和国内外研究现状。
Methods of data cleaning, data integration and transformation, and data reduction are discussed.
讨论数据清理、数据集成和变换、数据归约的方法。
Finally, the future research topics and application related to data cleaning problems are discussed.
并对今后数据清洗的研究和应用进行展望。
Absrtact: the prominent features of the data cleaning system are manifested in extendibility and interactivity.
摘要：可扩展性和可交互性是数据清洗系统的主要特征。
The construction of a data warehouse requires data integration, data cleaning, and data consolidation.
数据仓库的构造需要数据集成、数据清理、和数据统一。
This paper put much emphasis on the research and design of the data cleaning framework which can be extensible and customized.
本文的重点是对可扩展可定制数据清洗框架的研究与设计。
As early as four years ago, we spent a lot of time to break the island of information, data cleaning, integration, analysis and mining.
早在四年前，我们就花了大量时间来打破信息孤岛，对数据进行清洗、整合、分析挖掘。
By describing the definition and execution of cleaning rules, this article also expatiates the architecture of the data cleaning framework.
并通过描述清洗规则的定义和执行，详细阐述了该清洗框架的结构。
Combined the examples, data cleaning, user identification, session identification, path completion and transaction identification are discussed deeply.
结合实例详细介绍了数据净化、用户识别、会话识别、路径补充和事务识别等数据预处理技术。
Through data extraction, data cleaning, data conversion and data loading the data pre-processing platform integrate the original data into the data warehouse.
数据预处理平台通过数据抽取、数据清洗、数据转换和数据加载等方法整合并转换现有数据资源至数据仓库。
Basic data cleaning and data validation function was developed to meet data quality demand referring to data integrality, data authenticity and data time effectiveness.
为了保证数据质量，系统已建立初步的数据清理转换与数据源回溯验证功能，保证数据仓库数据的正确性、完整性和实效性。
Chapter 3 introduces the processes of data preprocessing, they are data cleaning, user identification, user session identification, path supplement and data formatting.
第三章探讨了数据预处理的流程，即数据清理、用户识别、用户会话识别、路径补充和数据格式化。
Data cleaning is a major issue.
清理数据是个大问题。
Data cleaning and data integration techniques are applied to ensure consistency in naming conventions, encoding structures, attribute measures, and so on.
使用数据清理和数据集成技术，确保命名约定、编码结构、属性度量的一致性等。
Data cleaning and data integration techniques are applied to ensure consistency in naming conventions, encoding structures, attribute measures, and so on.
使用数据清理和数据集成技术，确保命名约定、编码结构、属性度量的一致性等。
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