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。
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。
Abstract: Design of cleaning device based on the related data cleaning ginger access and correct analysis.
摘要：通过对生姜清洗的相关资料的查阅与正确分析，设计了清洗装置。
The last is lack of management of metadata, so the users cann't analyse or adjust the data cleaning processes.
缺少元数据管理，用户很难分析和逐步调整数据清洗过程。
This method not only finishs data acquisition agilely and rightly, but also finishs data cleaning in the same.
该方法不仅可灵活、准确地完成数据采集，还同时完成了数据的清理工作。
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。
Including business requirement, existing data cleaning, data sample, data explore build model and model valuation.
包括模型的业务要求、源数据的获取和清洗、数据采样、数据探索、模型建立及模型的评估。
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。
One is lack of human interaction, so users cant control the data cleaning processes and cant solve the exceptions in the processes;
以往数据清洗工具在三个方面存在不足：工具和用户之间缺少交互，用户无法控制过程，也无法处理过程中的异常；
The data preprocessing can define as the operations as followings: data cleaning, data integration, data conversion, data reduction.
数据的预处理主要是进行数据清理、数据集成、数据转换、数据归约等操作。
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。
This research accomplished such functions as information retrieval and analysis, data cleaning and importing into database from raw XML documents.
本研究实现了从原始心电的XML文件中的信息提取和解析，数据的清洗和导入数据库的功能。
Curre nt solutions for data cleaning require many iterated data-quality analysis so as to find errors, and long-running transformations to fix them.
当今数据清理方案需要反复进行数据质量分析以查找错误，为修复它们而进行的转换需要运行很长的时间。
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。
Detailed and exact metadata is absolutely necessarily for the creation, the data loading, the data cleaning and regular maintenance of a data warehouse.
详细而准确的元数据对于数据仓库的创建、数据加载、运行维护、清理脏数据等工作都必不可少。
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。
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 and transformation is an important area of data warehouse, the method for detecting approximately duplicate database record is one of technology difficulties.
数据清理转换是数据仓库中的一个重要研究领域，其技术难点之一是重复记录的识别。
It first describes the data cleaning and data selection briefly, then discusses the data preprocessing and data representation as detail as possible, at last, introduces the data set management.
首先简要介绍数据清洗与选择的基本方法，然后详细论述数据预处理、数据表示和数据集管理等方面的问题。
Designing data verification and cleaning.
设计数据验证和清理。
The product would be used during the master data integration phase, harmonizing and cleaning master data prior to being loaded into the MDM system.
在主数据集成阶段，在把主数据装载到MDM系统中之前，使用这个产品协调和清理主数据。
In the data world, the traditional approach to data management is largely about periodic cleaning. A better, policy-centric approach is data governance.
在数据世界里，数据管理的惯用方法基本上是定期清理。而一个更好的，以策略为中心的方法就是数据治理。
This can include invalidating sessions, canceling timers, canceling callback handlers or cleaning up any data structures that may need it.
清理可能包括终止会话、取消计时器、取消回调处理程序或清理可能需要的任何数据结构。
After every test case, the application under test is restored to its initial state, which involves cleaning up the test data created during the test case run.
每次测试之后，测试程序都会恢复到初始的状态，这就会在测试用例运行期间清除所有创建的测试数据。
The cleaning process covers a wide range of potential data problems, such as handing tricky backslashes, weird spaces, HTML, carriage returns, and more.
清理过程可以解决许多潜在的数据问题，比如处理棘手的反斜杠、离奇的空格、HTML、回车等等。
By cleaning up your data and ignoring data submitted improperly, you have made excellent first steps in securing your application.
通过清理数据并忽略被错误提交的数据，已经为保护应用程序奠定了良好的基础。
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