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Workflow pattern mining using e-mail communications.

机译:使用电子邮件通信进行工作流模式挖掘。

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摘要

The objective of this work is to utilize the abundant "unstructured" information and convert it into a logical and structured representation. This not only results in useful data representation to discover hidden patterns but also assists in precise decision making and optimization of the workflows. The problem today is not lack of data, but instead lack of structured information and data overload. In this study, we consider organizational emails as the source of data, since they are recognized to be a good source for inter organizational communication and workflows. Emails capture people's communication history that provides valuable insight regarding the infrastructure of an organization.;We considered threaded emails as the basic entity and basis for our pattern recognition algorithm. After exploring many classical graph matching approaches, we developed a method to measure the similarity among threaded emails. The algorithm of similarity measure is developed on the foundation of edge matching distance. The similarity measure is then utilized for efficient clustering of isomorphic and sub-isomorphic email representations. We validated the clustering efficiency by implementing and analyzing Silhouette index. Workflow and communication patterns have been developed after combining the graphs contained in distinct clusters.;The software development is done in Java utilizing Jung (Java API for graphical representation). The open source Pajek software is used to collect the network statistics. The Graphs are represented in universal Pajek format and result can be visualized using any Pajek reader software. This provides the opportunity to explore the results even in great detail. Users have ability to visualize the patterns at each stage: consolidated communication pattern, threaded email communication and identified workflow or communication patterns. The visualization gives the user a better sense of email archive and social networks. These patterns also represent distinct networks within the organization based on their communication interaction irrespective of their organizational or functional responsibilities.
机译:这项工作的目的是利用大量的“非结构化”信息并将其转换为逻辑和结构化的表示形式。这不仅导致有用的数据表示以发现隐藏的模式,而且还有助于精确的决策制定和工作流程的优化。今天的问题不是缺乏数据,而是缺乏结构化信息和数据过载。在本研究中,我们将组织电子邮件视为数据源,因为它们被认为是组织间通信和工作流的良好来源。电子邮件记录了人们的交流历史,为组织的基础架构提供了宝贵的见识。;我们将线程化电子邮件视为模式识别算法的基本实体和基础。在探索了许多经典的图形匹配方法之后,我们开发了一种方法来测量线程化电子邮件之间的相似性。在边缘匹配距离的基础上开发了相似度度量算法。然后将相似性度量用于同构和亚同构电子邮件表示的有效聚类。我们通过实施和分析Silhouette索引验证了聚类效率。在组合了不同集群中包含的图形之后,已经开发了工作流和通信模式。软件开发是使用Java(用于图形表示的Java API)在Java中完成的。开源的Pajek软件用于收集网络统计信息。这些图形以通用的Pajek格式表示,并且可以使用任何Pajek阅读器软件将结果可视化。这提供了机会,甚至可以非常详细地探索结果。用户可以在每个阶段可视化模式:合并的通信模式,线程化的电子邮件通信以及已标识的工作流或通信模式。可视化使用户对电子邮件存档和社交网络有了更好的了解。这些模式还基于它们之间的通信交互来代表组织内的不同网络,而不管其组织或职能职责如何。

著录项

  • 作者

    Mishra, Akhil.;

  • 作者单位

    State University of New York at Buffalo.;

  • 授予单位 State University of New York at Buffalo.;
  • 学科 Engineering Industrial.;Sociology Organizational.;Computer Science.
  • 学位 M.S.
  • 年度 2008
  • 页码 118 p.
  • 总页数 118
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 一般工业技术;自动化技术、计算机技术;
  • 关键词

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