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Research on information management of Digital Archives Based

时间:2015-10-15 来源:未知 编辑:梦想论文 阅读:
Abstract: with the development of socialist modernization, the development of computer technology, information technology plays a very important role in various fields of social development. Informatization construction is to be listed as the main content of the economic and social development. Management Webpage files including document translation, text, pictures, audio and video materials, multimedia teleconferencing. Especially the university archives to focus more on teaching and scientific research, management Webpage archives is the inevitable trend.

在信息化发展的今天,图书馆,特别是大学图书馆不仅要对信息进行简单的数字转换和管理,更要对新兴事物网络进行档案化管理和归档,包括文档、文字翻译转换、图片资料、声像资料、多媒体远程会议等。所以网络档案化管理,成为当今图书管理的必然趋势,这就必须对档案化管理的技术和法律相关问题进行深入阐述和探讨。
In the information technology development today, library, especially the university libraries should not only simple digital conversion and management of information, but also to the archives management and archiving of emerging thing of network, including document translation, text, picture information, audio-visual, multimedia teleconferencing. So the Internet Archive Management, become inevitable trend of the library management, it must be on the problem of archives management of technical and legal related in-depth exposition and discussion.


The so-called data mining (Data Mining), is from the large, incomplete, noise, fuzzy and random data, in which the extraction of implicit, previously unknown but potentially useful information and knowledge. These data may be structured, such as data in a relational database, and can also be semi structured, such as text, graphics, image data, and even heterogeneous data distribution in the network. The method can be found knowledge of mathematics, can also be non mathematics; can be interpreted, can also be summed up. Discovered knowledge can be used for information management, query optimization, decision support, process control, maintenance can also carry on the data of its own. Data mining research achievements over the years by means of mathematical statistics technology and artificial intelligence, knowledge engineering and other areas of the construction of the theoretical system of its own, is a cross subject involving database, artificial intelligence, statistics, mechanics, artificial neural network, visualization, parallel computing etc, is one of the most advanced research directions of the current international database and decision support field.

一、数据挖掘的功能
A, data mining function

数据挖掘通过预测未来趋势及行为,做出预测性的、基于知识的决策。数据挖掘的目标是从数据库中发现隐含的、有意义的知识,按其功能可分为以下几类。
Data mining by predicting the future trend and behavior, knowledge based decision making predictive. The goal of data mining is to find hidden, meaningful knowledge from the database, its functions can be divided into the following categories.


1、关联分析
1, correlation analysis

关联分析能寻找到数据库中大量数据的相关联系,常用的一种技术为关联规则和序列模式。关联规则是发现一个事物与其他事物间的相互关联性或相互依赖性。
Correlation analysis to find related data in the database, a technique commonly used for association rules and sequential patterns. Association rules are discovered one thing and the other things the mutual relevance or interdependence.

2、聚类
2, clustering

输入的数据并无任何类型标记,聚类就是按一定的规则将数据划分为合理的集合,即将对象分组为多个类或簇,使得在同一个簇中的对象之间具有较高的相似度,而在不同簇中的对象差别很大。聚类增强了人们对客观现实的认识,是概念描述和偏差分析的先决条件。聚类技术主要包括传统的模式识别方法和数学分类学。
The input data does not have any type of marker, cluster is according to certain rules to divide the data for the reasonable collection, about to group objects into classes or clusters, so that has a high similarity between objects in the same cluster, and objects in different clusters. Clustering increases people's understanding of the objective reality, is a concept description and deviation analysis prerequisites. Clustering techniques mainly include pattern recognition method and mathematical taxonomy of traditional.

3、自动预测趋势和行为
3, automatic forecasting trend and behavior

数据挖掘自动在大型数据库中进行分类和预测,寻找预测性信息,自动地提出描述重要数据类的模型或预测未来的数据趋势,这样以往需要进行大量手工分析的问题如今可以迅速直接由数据本身得出结论。
Data mining, automatic classification and prediction in a large database, finding predictive information, automatically is presented to describe the important data model or predict the trend of future data, so before the need for a large number of manual analysis problem can now quickly and directly by the data the conclusion.

4、概念描述
4, concept description

对于数据库中庞杂的数据,人们期望以简洁的描述形式来描述汇集的数据集。概念描述就是对某类对象的内涵进行描述并概括出这类对象的有关特征。概念描述分为特征性描述和区别性描述,前者描述某类对象的共同特征,后者描述不同类对象之间的区别。生成一个类的特征性只涉及该类对象中所有对象的共性。生成区别性描述的方法很多,如决策树方法、遗传算法等。
For the complex data in the database, is expected to form a concise description to describe the pooled data set. The concept is to describe certain types of objects are described and summarized the connotation about the characteristics of this kind of object. Concept Description Description Description for the characteristics and difference, the former describes the common features of a class of objects, the difference between the latter describe similar objects. Characteristic generates a class only relates to all objects of the class object in common. Many methods to generate the difference described, such as genetic decision tree method, algorithm.

5, deviation detection

数据库中的数据常有一些异常记录,从数据库中检测这些偏差很有意义。偏差包括很多潜在的知识,如分类中的反常实例、不满足规则的特例、观测结果与模型预测值的偏差、量值随时间的变化等。偏差检测的基本方法是寻找观测结果与参照值之间有意义的差别。这常用于金融银行业中检测欺诈行为,或市场分析中分析特殊消费者的消费习惯。


The data in the database often has some abnormal records from the database, the detection of these deviations is very meaningful. Deviation includes many potential knowledge, such as abnormal instance classification rules, does not meet the special case, observations, and the deviation of model prediction value changes with time etc.. The basic method is to look for the observed deviation detection result and the reference meaningful difference value between. This is often used to detect financial fraud in the banking industry, analyses the special consumer spending habits or market analysis.

二、数据挖掘在建设现代化高校档案馆中的应用
Two, the application of data mining in the modernization construction of university archives.

1、资源类数据包括馆藏档案经过数字化加工而产生的各类电子档案、电子文件中心中存储的各类电子档案、档案软件收集的信息、档案信息网建设和维护信息。我们从研究大学档案用户的信息需求出发,数据挖掘为大学档案馆全面掌握和准确理解档案用户的信息需求提供了方法。
1, the resource data includes the collection file after digital processing and produce various kinds of electronic archives, electronic records center in all kinds of electronic archives, archival storage software collected information, archives information network construction and maintenance information. We start from the research of university archives user's information requirement, data mining provides a method for University Archives mastering and accurate understanding of Archives Users Information demand.

(1) 利用Web访问信息挖掘技术发现其中的关联模式、序列模式和Web访问趋势等,构建多维视图的用户兴趣模型。从而可以确定档案信息或服务受欢迎的程度,发现用户访问模式和用户需求的趋势,从不同侧面来研究用户的信息需求,为优化档案馆的档案信息资源建设提供了科学依据。
(1) using Web to access information mining techniques to discover the connection mode, series mode and Web access trends, user interest model constructing multidimensional view. To determine the file information or service popularity, the discovery of user access patterns and user demand trend, to research the users from different side information needs, providing scientific basis for the construction of the information resources of archives optimization of archives.

(2) 收集大学档案网web服务器保留的用户注册信息、访问记录,以及有关用户与系统交互的信息等原始数据,经过清洗、浓缩和转换形成便于统计分析的用户查阅数据库、日志数据库、用户定制信息库、用户反馈信息等各种数据集合。
(2) the collection of University Archives Network Web server reserved user registration information, access to records, and information about users interaction with the system as the original data, after washing, concentration and conversion to form statistical analysis of user access to databases, log database, user customized information database, the user feedback information and other data collection.

2、从建设大学档案馆馆藏信息资源出发,数据挖掘为大学档案馆提供了选择一条科学发展道路的重要依据。
Starting from 2, the information resources construction of university archives, data mining provides an important basis for choosing a scientific development road of University archives.

(1) 利用档案网和档案管理软件访问信息的挖掘分析出档案资源的利用率,将利用率高、需求量大的传统载体档案优先数字化。例如:通过对档案信息的访问记录、检索请求中用户请求失败的数据进行分析,按类统计档案拒用集和频繁利用集,结合聚集算法发现馆藏资源的缺漏,有针对性地补充和丰富档案信息资源。
(1) the mining archives network and file management software to access the information analysis of the utilization of archives resources rate, will use the traditional carrier archives rate is high, high demand priority digitization. For example: Based on the analysis of archival information access records, the search request user request failure data, according to the class of statistical archives rejected for sets and frequent use of set, combined with the aggregation algorithm found collection resource gaps, targeted to supplement and enrich the information resources of archives.

(2) 在大学档案馆藏管理过程中利用文本挖掘,运用关联、分类、聚类等方法,从海量档案信息中按照相关专题进行挖掘、分类、加工、整理和有序化重组,构建特色档案信息库及各类专题档案信息库等。
(2) in the process of university archives management by using text mining, association, classification, clustering and other methods, from the massive archives information in accordance with the relevant special subject to mining, classification, processing, finishing and orderly restructuring, the construction of archival information database and all kinds of special archives information library.

3、从做好大学档案馆信息管理工作的角度出发,数据挖掘为优化馆藏信息和对未来工作的预测发挥重要作用。
Starting from 3, do a good job of management of university archives information perspective, data mining for the optimization of collection information and prediction of future work play an important role.

(1) 在提供利用环节中,对用户每次借阅的信息进行关联分析,发现各类档案信息之间的关联规则或比例关系,这样可以进一步优化馆藏信息。
(1) in using link, the user each time borrowing information for association analysis, association rule discovery or proportion of all kinds of archives information relationship, this can further optimize the collection information.

(2) 开展大学档案馆馆藏信息文本特征的建立、特征提取、特征匹配、特征集缩减和模型评价工作,实现对大量文档集合的内容进行总结、分类、聚类、关联分析、分布分析,通过归纳与总结,发现的知识可以为未来档案工作的趋势进行预测。
(2) the development of university archives information text feature establishment, feature extraction, feature matching, feature set reduction and model evaluation work, the realization of the large collection of documents of the content of the summary, classification, clustering, association analysis, distribution analysis, through the induction and conclusion, the discovered knowledge can be predicted for the trend of archives work in the future.

三、数据挖掘在管理类数据中的应用
Three, the application of data mining in management in data

大学档案馆的管理类数据包括:智能监控系统、消防系统、温湿度控制系统、智能密集架、数据管理系统、数据利用系统等在日常工作产生大量的管理类数据。我们得用数据挖掘工具在这类看似无用的数据中提取有价值的知识并运用到大学档案馆工作中,并在大学档案馆的现代化建设中发挥作用。


Management data of University Archives include: intelligent monitoring system, fire protection system, temperature and humidity control system, intelligent, data management system, data management system etc. by generating a large amount of data in daily work. We need tools to extract valuable knowledge in this kind of useless data and its application to the university archives work in data mining, and play a role in the modernization construction of University archives.


The focus of university archives work is the service for teachers and students, to serve as the center to carry out the work, how to use the advanced tools, improve the quality of services is baffled us. The data provide effective mining method for the university archives work intelligent, personalized, quality. In intelligent retrieval system can call the user interest model, automatic correction of retrieval strategy and based on user interest will be the retrieval results fast clustering and classification, and organized to sort out; for Design Institute, Academy of Social Sciences and other scientific research archives users, can use data mining to carry out targeted archives information mining, and apply the research results to outline, results report form is provided to the user. It not only realizes two time development of university archives, also will give the user to bring unexpected surprises.


Exchange of documents between the network initially scientists and researchers of the software, the Internet for education and research can get government subsidies. In Chinese, university funding, with university support library, digital library network archives is not profitable, output is the long-term social benefits of teaching and scientific research. Today, the Internet has become more and more commercialized, the network has become a potential technology investment object in the digital economy. Digital Library of university can also consider establishing for-profit network archive, using some business mode of the network business, such as network advertisement, banner ads, sponsorship advertising, subscriptions, B2C etc.. Income can be used for rolling development of university network construction of Digital Library's archives. At present people on these budding economic model know little. Public policy making body is the network management of government departments, the implementation of e-government, the development of cyber source, promote the transfer of publishing from the text printing to the network is an important task for the relevant government departments. Policy, attitude and measures of university is crucial to the development of digital library. The market means and policy of balance is the museum construction of network archives, archives operation of network, online content delivery and preservation should and must be considered.

参考文献:
Reference.

[1]Peer to peer Networking and Digital Right Management,by Michael A. Einhorn, Bill Rosenblatt, Policy Analysis No.534, CATO Institute. Fabruary 17,2005


[1]Peer to peer Networking and Digital Right Management, by Michael A. Einhorn, Bill Rosenblatt, Policy Analysis No.534, CATO Institute. Fabruary 172005

[2]What Every Citizen Should Know About DRM, aka Digital Right Management, by Mike Godwin Seuitoo Technology Counsel At Public Knowledge, 30 January, 2008, Ebook-Computer & Internet,
[2]What Every Citizen Should Know About DRM, aka Digital Right Management, by Mike Godwin Seuitoo Technology Counsel At Public Knowledge, 30 January, 2008, Ebook-Computer & Internet,

[3]彼得·莱曼.把万维网档案化.信息化参考,2004(13)
[3] Peter Lyman. Think of the World Wide Web archives. Information reference, 2004 (13)

[4]周宏仁等.信息化助建和谐社会的14个着力点.中国信息界,2008(3)
[4] Zhou Hongren. 14 key points of information to help build a harmonious society. China information industry, 2008 (3)

[5]ITU-T Technology Watch Reports. 2006-2008, Telecommunication Standardization Policy Division, ITU Telecommunication Standardization Sector
[5]ITU-T Technology Watch Reports. 2006-2008, Telecommunication Standardization Policy Division, ITU Telecommunication Standardization Sector

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