Project Details
Projekt
Supporting Business Process Modeling with Pattern-oriented Recommender Systems (ProPoneRe)
Subject Area
Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
Term
from 2021 to 2024
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 445156547
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.
DFG Programme
Research Grants
