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A model to integrate data mining and on-line analytical processing: With application to real time process control.

机译：集成数据挖掘和在线分析处理的模型：应用于实时过程控制。

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Since the widespread use of computers in business and industry, a lot of research has been done on the design of computer systems to support the decision making task. Decision support systems support decision makers in solving unstructured decision problems by providing tools to help understand and analyze decision problems to help make better decisions. Artificial intelligence is concerned with creating computer systems that perform tasks that would require intelligence if performed by humans. Much research has focused on using artificial intelligence to develop decision support systems to provide intelligent decision support.; Knowledge discovery from databases, centers around data mining algorithms to discover novel and potentially useful information contained in the large volumes of data that is ubiquitous in contemporary business organizations. Data mining deals with large volumes of data and tries to develop multiple views that the decision maker can use to study this multidimensional data. On-line analytical processing (OLAP) provides a mechanism that supports multiple views of multi-dimensional data to facilitate efficient analysis. These two techniques together can provide a powerful mechanism for the analysis of large quantities of data to aid the task of making decisions.; This research develops a model for the real time process control of a large manufacturing process using an integrated approach of data mining and on-line analytical processing. Data mining is used to develop models of the process based on the large volumes of the process data. The purpose is to provide prediction and explanatory capability based on the models of the data and to allow for efficient generation of multiple views of the data so as to support analysis on multiple levels. Artificial neural networks provide a mechanism for predicting the behavior of non-linear systems, while decision trees provide a mechanism for the explanation of states of systems given a set of inputs and outputs. OLAP is used to generate multidimensional views of the data and support analysis based on models developed by data mining. The architecture and implementation of the model for real-time process control based on the integration of data mining and OLAP is presented in detail. The model is validated by comparing results obtained from the integrated system, OLAP-only and expert opinion. The system is validated using actual process data and the results of this verification are presented. A discussion of the results of the validation of the integrated system and some limitations of this research with discussion on possible future research directions is provided.
机译：由于计算机在商业和工业中的广泛使用，因此已经在计算机系统设计上进行了大量研究以支持决策任务。决策支持系统通过提供工具来帮助理解和分析决策问题以帮助做出更好的决策，从而支持决策者解决非结构化决策问题。人工智能与创建执行人类需要执行的任务的计算机系统有关。许多研究都集中在使用人工智能来开发决策支持系统以提供智能决策支持上。来自数据库的知识发现围绕数据挖掘算法，以发现包含在当代企业组织中的海量数据中的新颖且潜在有用的信息。数据挖掘处理大量数据，并尝试开发决策者可以用来研究多维数据的多个视图。在线分析处理（OLAP）提供了一种机制，该机制支持多维数据的多个视图以促进有效的分析。这两种技术一起可以提供强大的机制来分析大量数据，以帮助进行决策。这项研究使用数据挖掘和在线分析处理的集成方法，为大型制造过程的实时过程控制开发了一个模型。数据挖掘用于基于大量过程数据来开发过程模型。目的是提供基于数据模型的预测和解释能力，并允许有效生成数据的多个视图，以支持在多个级别上进行分析。人工神经网络提供了一种预测非线性系统行为的机制，而决策树则提供了一种机制来解释给定一组输入和输出的系统状态。 OLAP用于基于数据挖掘开发的模型生成数据的多维视图并支持分析。详细介绍了基于数据挖掘和OLAP集成的实时过程控制模型的体系结构和实现。通过比较从集成系统，仅OLAP和专家意见获得的结果来验证该模型。使用实际过程数据对系统进行验证，并给出验证结果。讨论了集成系统验证的结果以及本研究的一些局限性，并讨论了未来可能的研究方向。

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