@misc{BazhenovaZerbatoWeske2018, author = {Bazhenova, Ekaterina and Zerbato, Francesca and Weske, Mathias}, title = {Data-Centric Extraction of DMN Decision Models from BPMN Process Models}, series = {Business Process Management Workshops}, volume = {308}, journal = {Business Process Management Workshops}, publisher = {Springer}, address = {Berlin}, isbn = {978-3-319-74030-0}, issn = {1865-1348}, doi = {10.1007/978-3-319-74030-0_43}, pages = {542 -- 555}, year = {2018}, abstract = {Operational decisions in business processes can be modeled by using the Decision Model and Notation (DMN). The complementary use of DMN for decision modeling and of the Business Process Model and Notation (BPMN) for process design realizes the separation of concerns principle. For supporting separation of concerns during the design phase, it is crucial to understand which aspects of decision-making enclosed in a process model should be captured by a dedicated decision model. Whereas existing work focuses on the extraction of decision models from process control flow, the connection of process-related data and decision models is still unexplored. In this paper, we investigate how process-related data used for making decisions can be represented in process models and we distinguish a set of BPMN patterns capturing such information. Then, we provide a formal mapping of the identified BPMN patterns to corresponding DMN models and apply our approach to a real-world healthcare process.}, language = {en} } @article{BazhenovaZerbatoOlibonietal.2019, author = {Bazhenova, Ekaterina and Zerbato, Francesca and Oliboni, Barbara and Weske, Mathias}, title = {From BPMN process models to DMN decision models}, series = {Information systems}, volume = {83}, journal = {Information systems}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0306-4379}, doi = {10.1016/j.is.2019.02.001}, pages = {69 -- 88}, year = {2019}, abstract = {The interplay between process and decision models plays a crucial role in business process management, as decisions may be based on running processes and affect process outcomes. Often process models include decisions that are encoded through process control flow structures and data flow elements, thus reducing process model maintainability. The Decision Model and Notation (DMN) was proposed to achieve separation of concerns and to possibly complement the Business Process Model and Notation (BPMN) for designing decisions related to process models. Nevertheless, deriving decision models from process models remains challenging, especially when the same data underlie both process and decision models. In this paper, we explore how and to which extent the data modeled in BPMN processes and used for decision-making may be represented in the corresponding DMN decision models. To this end, we identify a set of patterns that capture possible representations of data in BPMN processes and that can be used to guide the derivation of decision models related to existing process models. Throughout the paper we refer to real-world healthcare processes to show the applicability of the proposed approach. (C) 2019 Elsevier Ltd. All rights reserved.}, language = {en} } @phdthesis{Bazhenova2018, author = {Bazhenova, Ekaterina}, title = {Discovery of Decision Models Complementary to Process Models}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-410020}, school = {Universit{\"a}t Potsdam}, year = {2018}, abstract = {Business process management is an acknowledged asset for running an organization in a productive and sustainable way. One of the most important aspects of business process management, occurring on a daily basis at all levels, is decision making. In recent years, a number of decision management frameworks have appeared in addition to existing business process management systems. More recently, Decision Model and Notation (DMN) was developed by the OMG consortium with the aim of complementing the widely used Business Process Model and Notation (BPMN). One of the reasons for the emergence of DMN is the increasing interest in the evolving paradigm known as the separation of concerns. This paradigm states that modeling decisions complementary to processes reduces process complexity by externalizing decision logic from process models and importing it into a dedicated decision model. Such an approach increases the agility of model design and execution. This provides organizations with the flexibility to adapt to the ever increasing rapid and dynamic changes in the business ecosystem. The research gap, identified by us, is that the separation of concerns, recommended by DMN, prescribes the externalization of the decision logic of process models in one or more separate decision models, but it does not specify this can be achieved. The goal of this thesis is to overcome the presented gap by developing a framework for discovering decision models in a semi-automated way from information about existing process decision making. Thus, in this thesis we develop methodologies to extract decision models from: (1) control flow and data of process models that exist in enterprises; and (2) from event logs recorded by enterprise information systems, encapsulating day-to-day operations. Furthermore, we provide an extension of the methodologies to discover decision models from event logs enriched with fuzziness, a tool dealing with partial knowledge of the process execution information. All the proposed techniques are implemented and evaluated in case studies using real-life and synthetic process models and event logs. The evaluation of these case studies shows that the proposed methodologies provide valid and accurate output decision models that can serve as blueprints for executing decisions complementary to process models. Thus, these methodologies have applicability in the real world and they can be used, for example, for compliance checks, among other uses, which could improve the organization's decision making and hence it's overall performance.}, language = {en} }