181 research outputs found

    Verification and Simplification of DMN Decision Tables

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    Decision Model and Notation (DMN) on standardne notatsioon, mida kasutatakse √§rirakendustes otsuste loogika kirjeldamiseks. Otsustabelid on DMNi √ľks peamisi osi. DMNi otsustabelite suurenev kasutatavus igap√§evaste √§riotsuste √ľlesm√§rkimiseks ja automatiseerimiseks on t√Ķstatanud vajadust anal√ľ√ľsida otsustabeleid. See l√Ķput√∂√∂ annab √ľlevaate DMN otsustabelist ja kirjeldab kolme skaleeruvat algoritmi, mis on m√Ķeldud leidmaks kattuvaid reegleid ja puuduvaid reegleid ning lihtsustada otsustabeleid kasutades reeglite √ľhendamist. K√Ķik v√§lja pakutud algoritmid on implementeeritud avatud l√§htekoodiga DMN redaktorisse ja katsetatud suurte otsustabelite peal, mis p√§rinevad krediidiandmise andmebaasist.The Decision Model and Notation (DMN) is a standard notation to specify decision logic in business applications. A central construct in DMN is a decision table. The rising use of DMN decision tables to capture and to automate everyday business decisions raises the need to support analysis tasks on decision tables. This thesis provides scalable algorithms to tackle three analysis tasks: detection of overlapping rules, detection of missing rules and simplification of decision tables via rule merging. All proposed algorithms have been implemented in an open-source DMN editor and are tested on large decision tables derived from a credit lending data-set

    What we know and what we do not know about DMN

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    The recent Decision Model and Notation (DMN) establishes business decisions as first-class citizens of executable business processes. This research note has two objectives: first, to describe DMN's technical and theoretical foundations; second, to identify research directions for investigating DMN's potential benefits on a technological, individual and organizational level. To this end, we integrate perspectives from management science, cognitive theory and information systems research

    Enhancing declarative process models with DMN decision logic

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    Modeling dynamic, human-centric, non-standardized and knowledge-intensive business processes with imperative process modeling approaches is very challenging. Declarative process modeling approaches are more appropriate for these processes, as they offer the run-time flexibility typically required in these cases. However, by means of a realistic healthcare process that falls in the aforementioned category, we demonstrate in this paper that current declarative approaches do not incorporate all the details needed. More specifically, they lack a way to model decision logic, which is important when attempting to fully capture these processes. We propose a new declarative language, Declare-R-DMN, which combines the declarative process modeling language Declare-R with the newly adopted OMG standard Decision Model and Notation. Aside from supporting the functionality of both languages, Declare-R-DMN also creates bridges between them. We will show that using this language results in process models that encapsulate much more knowledge, while still offering the same flexibility

    Application of Logic-Based Methods to Machine Component Design

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    This paper describes an application worked out in collaboration with a company that produces made-to-order machine components. The goal of the project is to develop a system that can support the company\u27s engineers by automating parts of their component design process. We propose a knowledge extraction methodology based on the recent DMN (Decision Model and Notation) standard and compare a rule-based and a constraint-based method for representing the resulting knowledge. We study the advantages and disadvantages of both approaches in the context of the company\u27s real-life application

    Context-Aware Verification of DMN

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    The Decision Model and Notation (DMN) standard is a user-friendly notation for decision logic. To verify correctness of DMN decision tables, many tools are available. However, most of these look at a table in isolation, with little or no regards for its context. In this work, we argue for the importance of context, and extend the formal verification criteria to include it. We identify two forms of context, namely in-model context and background knowledge. We also present our own context-aware verification tool, implemented in our DMN-IDP interface, and show that this context-aware approach allows us to perform more thorough verification than any other available tool

    Applying the Decision Model and Notation in Practice: A Method to Design and Specify Business Decisions and Business Logic

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    Proper decision-making is one of the most important capabilities of an organization. Therefore, it is important to make explicit all decisions that are relevant to manage for an organization. In 2015 the Object Management Group published the Decision Model and Notation (DMN) standard that focuses on modelling business decisions and underlying business logic. DMN is being adopted at an increas-ing rate, however, theory does not adequately cover activities or methods to guide practitioners mod-elling with DMN. To tackle this problem this paper presents a method to guide the modelling process of business decisions with DMN. The method has been validated and improved with an experiment using thirty participants. Based on this method, future research could focus on further validation and improvement by using more participants from different industries

    Modeling, Executing and Monitoring IoT-Driven Business Rules in BPMN and DMN: Current Support and Challenges

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    The involvement of the Internet of Things (IoT) in Business Process Management (BPM) solutions is continuously increasing. While BPM enables the modeling, implementation, execution, monitoring, and analysis of business processes, IoT fosters the collection and exchange of data over the Internet. By enriching BPM solutions with real-world IoT data both process automation and process monitoring can be improved. Furthermore, IoT data can be utilized during process execution to realize IoT-driven business rules that consider the state of the physical environment. The aggregation of low-level IoT data into processrelevant, high-level IoT data is a paramount step towards IoT-driven business processes and business rules respectively. In this context, Business Process Modeling and Notation (BPMN) and Decision Model and Notation (DMN) provide support to model, execute, and monitor IoTdriven business rules, but some challenges remain. This paper derives the challenges that emerge when modeling, executing, and monitoring IoT-driven business rules using BPMN 2.0 and DMN standards

    From BPMN process models to DMN decision models

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    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
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