工作流挖掘的起点是收集和处理工作流日志。
The beginning of workflow mining is to collect and format the workflow log.
数据流频繁模式挖掘是数据流挖掘的基础研究之一。
Frequent pattern mining is one basic research of data stream mining.
提出基于点击流挖掘技术的商务智能辅助决策系统框架。
Present a system framework of intelligent DSS based on click-stream data mining technology.
最后，举例说明了数据流挖掘的应用，并展望了数据流挖掘未来的研究方向。
Finally, main applications and future research directions of data stream mining are pu...
近年来，数据流挖掘越来越引起研究人员的关注，已逐渐成为许多领域有用的工具。
Data stream mining has attracted many researchers 'attention and has become a useful tool for many fields.
工作流挖掘的目标是：倒转过程，收集和利用运行数据，从而支持工作流设计和分析。
The goal of workflow mining is to reverse the process and collect data at runtime to support workflow design and analysis.
工作流挖掘的目标是：倒转过程，收集和利用运行数据，从而支持工作流设计和分析。
The goal of workflow mining is to reverse the process and collect the data at runtime to support workflow design and analysis.
摘要：近年来，数据流挖掘越来越引起研究人员的关注，已逐渐成为许多领域有用的工具。
Absrtact: Data stream mining has attracted many researchers 'attention and has become a useful tool for many fields.
因此，针对仿真中常用的数据挖掘任务，研究时空效率高效的相应数据流挖掘算法具有重要意义。
Thus, it is important to research data stream mining algorithms having higher time and space efficiency, and to aim at resolving data mining tasks often used in system simulation.
工作流挖掘技术不是一个工作流设计的工具，但它对充分理解现有业务过程执行情况有很大的帮助。
The workflow mining technology is not just a tool of workflow design, but it is very useful for understanding the current business processes.
工作流挖掘技术不是一个工作流设计的工具，但它对充分理解现有业务过程执行情况有很大的帮助。
The workflow mining technology is not just a tool of workflow design, but also it is very useful for understanding the current business processes.
传统面向静态数据集的算法无法直接用于挖掘数据流，而现有数据流挖掘算法存在时空效率不高的缺陷。
Traditional data mining algorithms aiming at static datasets can't be used to mine data streams directly, neither do they have the time and space efficiency.
工作流挖掘技术是从工作流日志重构流程模型的方法，可以作为识别角色的方法，以尽量减少主观性的影响。
Workflow mining is a technique that reconstructs process model on the basis of workflow log. This method is used to identify roles, so that the role identification can be as objective as possible.
首先阐述了相关概念，接着提出了一种基于动态数据流挖掘的案例推理模型，其中动态数据流挖掘算法采用改进的数据流聚类算法。
This paper describes the relevant concepts and presents a model of CBR based on dynamic data stream mining, and gives an improved clustering algorithm of data stream.
数据流的连续、快速、无限、未知的特点决定了传统的数据挖掘技术已经不适合数据流挖掘，分析和挖掘数据流已经成为热点研究问题。
Data streams are continuous, fast, unlimited, unknown, so traditional technology of data mining is not suitable to data stream mining. Analysis and mining data stream has been a popular research.
通过此模型使用基于动态数据流挖掘的案例推理技术，对数据进行实时挖掘，产生连续、动态的临时案例库，实现知识库的实时更新，从而满足实际问题变化的需要。
Through this model the system can mine real-time datum, produce continuous, dynamic temporary cases, update the knowledge base in real time and meet the needs of the practical problems.
然后，在一个挖掘流中使用这个规则文件，把概念从文本列中提取到关系数据库表中。
We will then use this rule file in a mining flow to extract the concepts from text columns in relational database tables.
为了实现动态调用，需要创建一个存储过程，它接受这两个参数并通过此用户输入调用挖掘流。
To enable dynamic invocation, you should create a stored procedure that takes two parameters and invokes the mining flow with this user input.
保存这个挖掘流。
这个存储过程使您能够用用户定义的属性设置来动态调用前面设计的挖掘流。
The stored procedure enables you to invoke the previously designed mining flow dynamically with user-defined property Settings.
接下来，执行挖掘流。
通过使用这些操作符，您可以构建一个挖掘流，只需要将这些操作符拖拽到编辑器的工作区中。
With these operators you can build up a mining flow by dragging and dropping operators to the editor canvas.
可以通过将这些操作符拖放到编辑器画布上来构建一个挖掘流。
With these operators, you can build up a mining flow by dragging and dropping them on the editor canvas.
针对“RETAIL_RULES”的一个表目标操作符现在应被显示在挖掘流中。
A table target operator for "RETAIL_RULES" should now be shown in the mining flow.
图12显示完成后的挖掘流。
至此，挖掘流就已经准备好，可以执行了。
挖掘流现在应该看上去与图12所示的相似。
The mining flow should now look similar to what is shown in Figure 12.
为评价创建一个新的挖掘流。
调整这个存储过程来执行前面定义的挖掘流。
Adapt the stored procedure to execute the previously defined mining flow.
稍后，在Cognos中，您可能会想用用户定义的输入来动态调用前面设计的这个挖掘流。
Later, within Cognos, you may want to invoke your previously designed mining flow dynamically with user-defined input.
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