企业电子邮件归档问题探讨

随着电子邮件成为全球企业内部交流、以及企业与外部(包括客户和商业伙伴)信息往来的最主要方式之一,电子邮件数量快速增长,如何安全高效地管理邮件信息,如何从大量邮件中快速搜索出所需的历史邮件和附件,是企业信息管理必须要面对的问题。而这些问题的处理前提,就是企业对电子邮件归档技术的应用。本文结合A公司邮件系统应用现状,对当前国际国内企业电子邮件归档技术的应用以及邮件归档技术的发展作了深入分析,并且针对邮件归档应用的必要性以及归档产品的选型进行了详细阐述。     

小心保管电子邮件也能成为证据

电子邮件被越来越频繁地应用在了企业的日常办公中,平均每名员工每天会收发几十封电子邮件,多媒体文件的增多使得电子邮件的体积增长为MB级别。另一方面,以电子邮件作为载体的重要文档越来越多,企业需要合理管理这些邮件包含的所有信息,以便在未来既能满足企业内部查询检索的需要,又能满足相关法规的监督与审计。为了达到这两方面的要求,企业的邮件系统正在面临新的挑战。     

企业邮件服务器搭建技术

随着信息技术的快速发展,邮件系统逐渐成为了信息交换和信息沟通中的重要工具,企业要实现内部和外部之间的信息交流都需要电子邮件。电子邮件和一般意义上的信件一样需要通过邮局来传送,在网络中电子邮件需要相应的邮件服务器来完成信息的提交、存储和转发等功能。企业邮件服务器作为企业信息化建设的重要组成部分,对于企业的形象以及发展具有重要的现实意义,加强对邮件服务器搭建技术的研究是非常必要的。     

eMail Station针对小企业

英特尔公司于11月中旬推出Intel InBusiness eMail Station,该设备大约与平装小说的大小相仿,面向少于50名员工的企业,可提供局域网和Internet电子邮件功能,其中包括自动发送和检索信息、远程拨入电子邮件以及其它优势。该设备在20分钟內即可安裝完毕。该款eMail Station拥有下列功能:自动发送/检索信息可以消除人工下载电子邮件的时间和成本。现在,电子邮件将出现在员工的     

基于本体的企业搜索引擎研究

随着企业的发展以及企业信息化建设的推进,Internet技术在越来越多的企业中得到应用,企业内部信息量以惊人的速度增长.面对企业内部海量信息,传统的搜索引擎无法满足企业用户对企业信息的检索需求,因此企业级的搜索引擎成为迫切需求.对传统的搜索引擎和本体知识进行学习,设计了一个基于本体的企业级搜索引擎系统模型.该设计运用本体知识,对检索关键词进行语义扩展,对检索结果进行语义相似性判断,最终实现搜索结果更加精确.     

查询子空间在图像检索中的应用

近几年来,为了解决图像检索系统中由底层视觉特征和高层语义之间的差异所造成的检索困难,从信息捡索中引入了相关反馈技术。在过去几年中,它在该研究领域取得了一定的成功。文章提出了一种利用反馈信息建立“查询子空间”的检索模型,它将用户的语义查询进行基于视觉特征的分类,构造多个“查询子空间”,这些子空间拥有自身的查询模型和检索模型,最后的检索结果根据这多个“查询子空间”的检索结果得到。该模型具有较强的灵活性、扩展性,有效地利用了用户的反馈信息,动态地建立了底层视觉特征和高层语义之间的映射,能适应不同用户的查询。同时,将负反馈信息合理地融入到该模型中,提高了系统的检索效率。实验结果表明采用该检索模型的系统相比现有的检索系统性能有了较大提高。     

企业电子邮件激增归档管理势在必行

随着电子邮件成为全球企业内部交流、以及企业与外部(包括客户和商业伙伴)信息往来的最主要方式之一,电子邮件数量快速增长,简单的备份已经不再能有效地满足企业的邮件管理需求,丢失邮件或无法及时查询所需邮件信息等状况时有发生,有的甚至因此而惹上官司。如何安全高效地管理邮件信息,如何从大量邮件中快速搜索出所需的历史邮件和附件,是企业信息管理必须要面对的问题。选用适合本企业的电子邮件归档管理系统是目前最常采用,也是最有效的解决方案。     

浅谈电子邮件的标准格式

在开发应用软件的时候，经常会遇到电子邮件，那么如何对电子邮件进行控制呢？本文将就电子邮件的标准形式与广大读者作一简单的探讨。电子邮件中的信息主要包括两个部分，其中主体部分是由一个Internet用户传递给另一个用户的信息，除此之外，电子邮件中还必须包含邮件附加的服务信息。SMTP服务器利用这些信息来传递邮件，而客户端的邮件接收软件则利用这些信息来对邮件进行分类。这些附加的数据用信头的形式被包含在邮件信头中。电子邮件的正文（发信的Internet用户写的信件）则紧随其后。如果使用OutlookExpress收发电子邮件，在收件…     

一种电子邮件敏感信息检测算法

针对目前电子邮件安全网关不能很好地支持敏感信息检测问题,深入研究了Winnow算法和Markov模型,在N-Gram语言模型的基础上,提出了一种邮件特征选择方法--Markov-Gram,该方法以句子为单位进行特征项的选取,不仅保留了更多的语义信息,而且可以有效地减少特征项的数目,解决"维度灾难"问题;提出一种Winnow算法训练过程中初始权重生成方法,该方法融入了电子邮件结构特点以及     

基于数据挖掘的邮件分类识别研究

在贝叶斯过滤技术的启发下,选择数据挖掘的方法来研究一种具有学习能力的邮件过滤技术.通过对电子邮件的分析和研究,提出对邮件结构字段信息和邮件正文信息加以离散和特征化处理,用向量的方式表示电子邮件,建立了一种基于信息熵的决策树邮件分类识别模型.     

Personal information retrieval: smartphones vs. computers,emails vs. files

Millions of people retrieve their emails and files using their smartphones, yet smartphone retrieval of such personal information has never been studied or compared to retrievals from PCs. In our within-subjects study, we compared the retrievals of our 57 participants in four conditions: files using PCs, emails using PCs, files using smartphones, and emails using smartphones. Our results indicate that when using smartphones, retrievals were significantly less successful and efficient than when using PCs, casting doubt on the implicit assumption that the use of these devices is equivalent. Our results also indicate that participants used the search facility for emails about seven times more than for files, which can encourage vendors to invest more efforts in improving email search engines and file navigation systems. Finally, we found that the tendency to search shows interpersonal differences but consistency across different situations for the same individual and therefore can be regarded as a personal trait. Future research can attempt to explain the search tendency trait in terms of cognitive abilities and personality traits, incorporating it to well-established theories. This may pave the way to a new trait-related theory in the field of information science.     

Social feature-based enterprise email classification without examining email contents

Without imposing restrictions, many enterprises find nonwork-related contents consuming network resources. Business communication over emails thus incurs undesired delays and inflicts damages to businesses, explaining why many enterprises are concerned with the competition to use email services. Obviously, enterprises should prioritize business emails over personal ones in their email service. Therefore, previous works present content-based classification methods to categorize enterprise emails into business or personal correspondence. Accuracy of these methods is largely determined by their ability to survey as much information as possible. However, in addition to decreasing the performance of these methods, monitoring the details of email contents may violate privacy rights that are under legal protection, requiring a careful balance of accurately classifying enterprise emails and protecting privacy rights. The proposed email classification method is thus based on social features rather than a survey of emails contents. Social-based metrics are also designed to characterize emails as social features; the obtained features are treated as an input of machine learning-based classifiers for email classification. Experimental results demonstrate the high accuracy of the proposed method in classifying emails. In contrast with other content-based methods that examine email contents, the emphasis on social features in the proposed method is a promising alternative for solving similar email classification problems.     

电子邮件动态分类系统的研究与应用

电子邮件已成为许多企业开展商务与办公的重要媒介,许多信息都保存在电子邮件系统。对大量邮件的管理,信息分类是一种有效的管理方法,但传统的人工文本分类方式相对静态且耗时较多。针对非结构化的邮件信息管理,提出采用动态分类体系,通过文本挖掘方法,开发一套基于多智能代理架构的电子邮件自动分类系统,提升邮件自动分类的效率。     

A multi-user perspective for personalized email communities

Email classification and prioritization expert systems have the potential to automatically group emails and users as communities based on their communication patterns, which is one of the most tedious tasks. The exchange of emails among users along with the time and content information determine the pattern of communication. The intelligent systems extract these patterns from an email corpus of single or all users and are limited to statistical analysis. However, the email information revealed in those methods is either constricted or widespread, i.e. single or all users respectively, which limits the usability of the resultant communities. In contrast to extreme views of the email information, we relax the aforementioned restrictions by considering a subset of all users as multi-user information in an incremental way to extend the personalization concept. Accordingly, we propose a multi-user personalized email community detection method to discover the groupings of email users based on their structural and semantic intimacy. We construct a social graph using multi-user personalized emails. Subsequently, the social graph is uniquely leveraged with expedient attributes, such as semantics, to identify user communities through collaborative similarity measure. The multi-user personalized communities, which are evaluated through different quality measures, enable the email systems to filter spam or malicious emails and suggest contacts while composing emails. The experimental results over two randomly selected users from email network, as constrained information, unveil partial interaction among 80% email users with 14% search space reduction where we notice 25% improvement in the clustering coefficient.     

‘Dealing with My Emails’: Latent user needs in email management.

Email is a communication channel that provides a number of benefits. It can be stored, retrieved and forwarded. It also allows a recipient to choose when to uptake communication and how to pace it. However, email also incurs one prevalent cost: the feeling of email overload. One of the reasons leading to that feeling lays in the fact that current email clients do not provide an inbox structure that facilitates email prioritization, information structuring and work-flow management. The goal of this study was to understand the latent user needs regarding handling emails. We identified six such needs: three pertaining to email organization (email annotation, reliable structure and no urgency to classify) and three related to email retrieval (informative overview, flexible sorting and efficient search). We further investigated the dominance, importance and dependencies between these needs. The results were then discussed and implications for future inbox design were proposed.     

基于邮件正文的邮箱用户别名抽取

邮箱用户身份信息挖掘是数据挖掘研究的一个热点。当前相关研究大多仅从邮件头中抽取邮箱用户的别名,遗漏了邮件正文中潜藏的更能代表通信双方身份的别名信息。针对纯文本邮件正文中邮箱用户别名信息抽取问题,提出了基于统计和规则过滤的称呼块和签名块定位算法,该算法能高效准确地从邮件正文中提取出蕴涵邮箱用户别名的称呼块和签名块文本片段;进一步提出了基于别名边界词汇模板修正的别名抽取方法,从而提高了仅基于命名实体识别或词性标注工具识别别名的准确率。实验结果表明,提出的方法可以有效地抽取出邮件正文中邮箱用户的别名。     

一种云环境中密文数据的模糊多关键词检索方案

越来越多的企业和个人用户将大量的数据存储在云服务器。为了保障数据隐私,重要数据以密文形式存储在云端,但却给数据检索操作带来严峻挑战。传统的基于明文的检索方案不再适用,已有的基于密文的检索方案存在不支持模糊检索或多关键词检索、效率较低、空间开销较大、不支持检索结果排序等问题。因此,研究安全高效的密文检索方法具有重要意义。提出了一种新的云环境中密文数据的模糊多关键词检索方案,该方案能够从云服务器上检索出包含有指定多个关键词的密文,支持模糊关键词检索,并且不会向云服务器和其他攻击者泄露与数据和检索相关的任何明文信息;使用计数型布隆过滤器和MinHash算法构建索引向量和查询向量,使得索引构建和查询过程更加高效,且排序结果更加准确。安全性分析和性能评估表明该方案具有高安全性、可靠性、检索效率和准确率。     

Intranet搜索引擎设计与实现

随着企业内部网络应用的深入,Intranet内部的信息资源越来越庞大,怎样为这些数据信息构建索引是我们面If缶的主要任务,而全文检索的产生解决了这一问题。目前越来越多的中小企业采用Linux系统作为Web平台,并且利用检索系统来管理内部繁多的文本和HTML文件。文章对www搜索引擎的全文检索及其相关技术进行了分析和讨论,实现了一个基于Linux环境的Intranet搜索引擎LISE（Linux Intranet Search Engine）。LISE利用了基于词表的索引方法,为用户提供了更加准确的信息,能满足多种中小企业用户的需求。     

基于图像信息度量与关键词的邮件智能分类系统

如何利用邮件的正文与附件信息有效地实现其分类,是现在邮件处理领域一个重要的课题。该文从商业应用角度提出了一种基于图像信息度量与关键词的邮件智能过滤与分发方法,通过基于朴素贝叶斯分类器的邮件关键词信息处理,及附件图像信息的基于归一化PIM文本图像检测理论的分析,能够综合运用邮件正文、地址等文本信息与附件图像信息作为分类的评价参数,有效地实现了邮件的智能分类。     

An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factors

Internet of Things (IoT) is gradually adopted by many organizations to facilitate the information collection and sharing. In an organization, an IoT node usually can receive and send an email for event notification and reminder. However, unwanted and malicious emails are a big security challenge to IoT systems. For example, attackers may intrude a network by sending emails with phishing links. To mitigate this issue, email classification is an important solution with the aim of distinguishing legitimate and spam emails. Artificial intelligence especially machine learning is a major tool for helping detect malicious emails, but the performance might be fluctuant according to specific datasets. The previous research figured out that supervised learning could be acceptable in practice, and that practical evaluation and users' feedback are important. Motivated by these observations, we conduct an empirical study to validate the performance of common learning algorithms under three different environments for email classification. With over 900 users, our study results validate prior observations and indicate that LibSVM and SMO-SVM can achieve better performance than other selected algorithms.