Predictive and Interactive Management of Potential Inconsistencies in Business Rules

业务规则（BR）通常用于为执行的流程应该遵守的公司规章或规则建模。实际的br集可以手工制作，从过程观察中挖掘，或者从自然语言规范中派生。由于这些都是容易出错的机制，因此生成的br集可能包含不一致和其他问题，这可能导致在流程执行期间根据这些规则验证流程时出现意外行为和代价高昂的调试。一种具有挑战性的问题类型是潜在的不一致性，即某些规则的矛盾结论，然而只有在执行过程中观察到某些事实组合时才会发生。在MIB中，我们解决了BRs中这类潜在不一致性的预测性和交互式管理。为此，我们将利用不一致测量（IM）和形式论证的方法，两者都是知识表示（KR）和推理领域的研究课题。特别是，我们将首先建立一个正式框架，用于研究潜在不一致的概念，在最先进的规则形式化（如DMN、Declare和FCL）中研究它，并分析诸如计算复杂性之类的属性。此外，我们将开发方法来分析、可视化，特别是测量潜在的不一致性，从而为建模者提供对潜在问题严重性的评估。此外，使用形式化论证的方法，我们将开发一个对话系统，该系统允许建模者以交互方式处理和解决规则库中的问题。这些方法将嵌入到BR基的创建阶段，以支持BR开发周期早期的建模者。这还包括将我们的方法集成到现有的规则挖掘算法中，并开发新的不一致感知算法。

Business rules (BR) are commonly used to model company regulations or rules that should be adhered to by executed processes. The actual set of BRs can be manually crafted, mined from process observations, or derived from natural language specifications. As these are error-prone mechanisms, the resulting set of BRs may however contain inconsistencies and other issues, which may lead to unexpected behaviour and costly debugging when processes are validated against these rules during process execution. A challenging type of issues are potential inconsistencies, i.e., contradictory conclusions of certain rules that will however only occur when certain fact combinations are observed during execution. In MIB we address the predictive and interactive management of such types of potential inconsistencies in BRs. For that, we will make use of approaches of inconsistency measurement (IM) and formal argumentation, both being research topics within the area of knowledge representation (KR) and reasoning. In particular, we will first set up a formal framework for investigating the notion of a potential inconsistency, investigate it in state-of-the-art rule formalisms such as DMN, Declare, and FCL, and analyze properties such as computational complexity. Further, we will develop approaches to analyze, visualize, and, in particular, measure potential inconsistencies to provide the modeler with an assessment on the severity of potential issues. Moreover, using methods from formal argumentation, we will develop a dialog system that allows the modeler to interactively address and solve the issues in the rule base. These methods will be embedded in the authoring phase of BR bases to support modelers early in the development cycle of BRs. This also includes the integration of our methods into existing rule mining algorithms and the development of novel inconsistency-aware ones.