Supporting Business Process Modeling with Pattern-oriented Recommender Systems (ProPoneRe)

所设想的研究项目propere的目标是开发一种业务流程建模方法，通过自动支持分析和建模建议来提高流程模型的质量。我们利用历史过程模型并分析其模型元素标签，利用自然语言处理（NLP）识别过程模式。模式在更高的抽象层次上表示典型的处理步骤（例如，“关于应用程序的决策”），并且可以由多个过程活动组成。为了识别模式，我们首先将流程模型元素标签中使用的动作词分配给抽象类别。一个过程模式由多个活动组成，其中包含不同类别的动作词。在技术上，我们通过使用NLP方法来识别动词、名词化、模糊表达和主题角色，以及通过分析历史过程模型来消除歧义，从而识别典型的过程活动。在第二步中，我们将已识别的活动包含为流程模式。在建模期间，将识别的模式与已经建模的模型片段进行比较。基于这些模式，我们开发了一个以不同方式支持建模者的推荐系统：首先，基于从历史过程模型中提取的知识，知道下一步可能或通常会建模的模型元素。推荐系统向建模者提出这些建议，这样可以确保建模者不会忘记流程的任何方面。由于建议只是建议，系统还将考虑新流程（即，推荐系统还不知道的不可预见的流程）。其次，由于历史过程知识，该系统可以识别建模者在建模过程中是否犯了错误，并相应地通知建模者。为了构建推荐功能，我们需要能够基于已经建模的流程模型片段和历史流程知识进行预测的技术（例如，关于接下来应该建模的其他模型元素或可能出现的错误）。因此，我们将基于n图和概率有限自动机多方法地开发推荐功能，并对其进行修改和扩展，使其适合于过程模型和过程模式。推荐系统将由测试人员和实践中的现有过程模型进行实施和评估，并作为编程库提供给学术界。

The goal of the envisioned research project ProPoneRe is to develop a business process modeling approach to increase the quality of process models through automatic support for analysis and modeling recommendations. We make use of historic process models and analyze their model element labels to identify process patterns using Natural Language Processing (NLP). The patterns represent typical processing steps on a higher level of abstraction (e.g., “decision about an application”) and may consist of multiple process activities. To identify the patterns, we first assign action words used in the labels of the process model elements to abstract categories. One process pattern consists of multiple activities containing action words of different categories then.Technically, we recognize typical process activities by using NLP methods to identify verbs, nominalizations, vague expressions, and thematic roles, as well as of disambiguation by analyzing historical process models. In a second step, we subsume the identified activities as process patterns. During modeling, the identified patterns are compared to the model fragments, which have already been modeled. Based on the patterns, we develop a recommender system that supports the modeler in different ways: Firstly, based on the knowledge extracted from the historic process models, it is known which model elements one would probably or typically model next. The recommender system proposes these to the modeler and this way assures that the modeler does not forget any aspects of the process. As the recommendations are only suggestions, the system will also account for processes that are new (i.e., that are unforeseen rep. unknown to the recommender system yet). Secondly, due to the historical process knowledge, such a system can recognize if the modeler made mistakes while modeling and inform the modeler accordingly. To build the recommender functionality, we need techniques that can make predictions (e.g., about additional model elements that should be modeled next or probable mistakes) based on already modeled process model fragments and the historical process knowledge. Therefore, we will develop the recommender functionality multi-methodically based on n-grams and probabilistic finite automatons, which will be both modified and extended to make them suitable for process models and process patterns. The recommender system will be implemented, evaluated with test persons and existing process models from practice and made available for the academic community as a programming library.