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首页> 外文期刊>Journal of Intelligent Information Systems >Model mining: Integrating data analytics, modelling and verification
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Model mining: Integrating data analytics, modelling and verification

机译：模型挖掘：集成数据分析，建模和验证

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Process mining techniques have been developed in the ambit of business process management to extract information from event logs consisting of activities and then produce a graphical representation of the process control flow, detect relations between components involved in the process and infer data dependencies between process activities. These process characterisations allow the analyst to discover an annotated visual representation of the conceptual model or the performance model of the process, check conformance with an a priori model to detect deviations and extend the a priori model with quantitative information such as frequencies and performance data. However, a process model yielded by process mining techniques is more similar to a representation of the process behaviour rather than an actual model of the process: it often consists of a huge number of states and interconnections between them, thus resulting in a spaghetti-like net which is hard to interpret or even read. In this paper we propose a novel technique, which we call model mining, to derive an abstract but concise and functionally structured model from event logs. Such a model is not a representation of the unfolded behaviour, but comprises, instead, a set of formal rules for generating the system behaviour, thus supporting more powerful predictive capabilities. The set of rules can be either inferred directly from the events logs (constructive mining) or refined by sifting a plausible a priori model using the event logs as a sieve until a reasonably concise model is achieved (refinement mining). We use rewriting logic as the formal framework in which to perform model mining and implement our framework using the Maude rewrite system. Once the final formal model is attained, it can be used, within the same rewriting logic framework, to predict future evolutions of the behaviour through simulation, to carry out further validation or to analyse properties through model checking. Finally, we illustrate our approach on two case studies from two different application fields, ecology and collaborative learning.
机译：流程挖掘技术已经在业务流程管理的范围中开发，以从活动中组成的事件日志中提取信息，然后生成过程控制流程的图形表示，检测过程中涉及的组件之间的关系，并在过程活动之间推断数据依赖性。这些过程特征允许分析师发现概念模型的注释视觉表示或过程的性能模型，检查一致性模型以检测偏差并扩展具有定量信息（如频率和性能数据）的先验模型。然而，通过过程挖掘技术产生的过程模型与过程行为的表示而不是过程的实际模型更类似于：它通常由它们之间的大量状态和互连组成，因此导致了一种类似的意大利面网难以解释甚至阅读。在本文中，我们提出了一种新颖的技术，我们呼叫模型挖掘，从事事件日志推导出抽象但简明且功能结构化的模型。这样的模型不是展开行为的表示，而是包括一组用于生成系统行为的正式规则，从而支持更强大的预测能力。这组规则可以直接从事件日志（建设性挖掘）推断，或者通过使用事件日志作为筛子来改变合理的先验模型，直到实现了合理简洁的模型（细化挖掘）。我们使用重写逻辑作为使用Maude Rewrite系统执行模型挖掘和实现我们的框架的正式框架。一旦获得最终的正式模型，它可以在相同的重写逻辑框架内使用，以通过模拟预测行为的未来演变，以进行进一步的验证或通过模型检查来分析属性。最后，我们在两种不同的应用领域，生态学和协作学习的两种案例研究中说明了我们的方法。

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