III-Core-Small: Collaborative Research: Mining and Optimizing Ad Hoc Workflows

自组织工作流在服务行业、科学研究以及日常生活中无处不在，如客户服务、故障排除、信息搜索等工作流，因此优化自组织工作流具有显著的社会效益。目前，临时工作流的执行是基于人工决策的，其中误解、缺乏经验和低效处理并不少见，导致操作效率低下。该研究项目的目标是设计和开发基本模型、概念和算法，以挖掘和优化临时工作流。本课题在以下几个关键领域进行了创新性的研究：(1)网络建模与结构挖掘。建立了一个网络模型，该模型统计地捕捉了自组织工作流的执行特征，并针对不同的优化目标对新的工作流进行了优化以提高其执行能力。(2)工作流人工产物挖掘。在建立在工作流执行基础上的网络模型的基础上，利用工作流人工挖掘技术进行扩展，实现了一个既能利用执行又能利用文本内容的优化系统。(3)角色发现与关系评估。为了进一步优化工作流，建立了一个计算框架来分析临时工作流执行中涉及的代理的角色和关系。该项目的进步包括表示临时工作流的模型、挖掘隐藏协作模型的算法以及优化临时工作流处理的技术。该项目连接了两个新兴的研究领域：服务科学和网络科学，并丰富了数据挖掘的原理和技术。它还通过来自不同领域(数据挖掘、数据库和网络)的团队成员的协作来增强研究基础设施。这项研究通过学生指导和课程开发与教育紧密结合。该项目产生的出版物、软件和课程材料将在项目网站上传播：网址：http://www.cs.ucsb.edu/~xyan/smartflow.htm

Ad hoc workflows are everywhere in service industry, scientific research, as well as daily life, such as workflows of customer service, trouble shooting, information search, etc. Optimizing ad hoc workflows thus has significant benefits to the society. Currently the execution of ad hoc workflows is based on human decisions, where misinterpretation, inexperience, and ineffective processing are not uncommon, leading to operation inefficiency. The goal of this research project is to design and develop fundamental models, concepts, and algorithms to mine and optimize ad hoc workflows. The project includes novel research on the following key areas: (1) Network Modeling and Structure Mining. A network model is built that statistically captures the execution characteristics of ad hoc workflows, and is optimized to improve the execution of new workflows with respect to different optimization objectives. (2) Workflow Artifact Mining. The network model built on workflow executions is then extended with workflow artifact mining to realize an optimization system that is able to take advantage of both executions and text contents. (3) Role Discovery and Relation Assessment. A computational framework is built to analyze the roles and relationships of agents involved in ad hoc workflow executions in order to further optimize workflows. Advances from this project include models to represent ad hoc workflows, algorithms for mining hidden collaborative models, and techniques that optimize ad hoc workflow processing. The project bridges two emerging research areas: service science and network science, and enriches the principles and technologies of data mining. It also enhances research infrastructure through the collaboration of team members from different areas (data mining, database, and network). This research is tightly integrated with education through student mentoring and curriculum development. Publications, software and course materials that arise from this project will be disseminated on the project website: URL: http://www.cs.ucsb.edu/~xyan/smartflow.htm