Sinds september 2015 is de ‘business rule management wereld’ / ‘decision management wereld’ weer een standaard rijker: The Decision Model and Notation (DMN). De Object Management Group (OMG) heeft deze nieuwe standaard uitgebracht met als doel een standaard taal te creëren om 1) requirements voor beslissingen en 2) de beslissingen zelf te modelleren. De adoptie van DMN heeft een wat lange aanloop gehad, maar begint nu serieuze vormen aan te nemen. Om deze reden brengen wij een vierdelige serie over DMN en het gebruik van DMN uit. In dit deel (deel 2) gaan we in op de basis principes die gelden bij het creëren van een DRD.
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Within recent years, Financial Credit Risk Assessment (FCRA) has become an increasingly important issue within the financial industry. Therefore, the search for features that can predict the credit risk of an organization has increased. Using multiple statistical techniques, a variance of features has been proposed. Applying a structured literature review, 258 papers have been selected. From the selected papers, 835 features have been identified. The features have been analyzed with respect to the type of feature, the information sources needed and the type of organization that applies the features. Based on the results of the analysis, the features have been plotted in the FCRA Model. The results show that most features focus on hard information from a transactional source, based on official information with a high latency. In this paper, we readdress and -present our earlier work [1]. We extended the previous research with more detailed descriptions of the related literature, findings, and results, which provides a grounded basis from which further research on FCRA can be conducted.
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Sinds september 2015 is de ‘business rule management wereld’ / ‘decision management wereld’ weer een standaard rijker: The Decision Model and Notation (DMN). De Object Management Group (OMG) heeft deze nieuwe standaard uitgebracht met als doel een standaardtaal te creëren om 1) requirements voor beslissingen en 2) de beslissingen zelf te modelleren. De adoptie van DMN heeft een wat lange aanloop gehad, maar begint nu serieuze vormen aan te nemen. Om deze reden brengen wij een vierdelige serie over DMN en het gebruik van DMN uit. In dit deel (deel 3) wordt er verder gegaan met stap 4. Wat zijn de benodigde feittype om de beslissing te nemen?
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Sinds september 2015 is de ‘business rule management wereld’ / ‘decision management wereld’ weer een standaard rijker: The Decision Model and Notation (DMN). De Object Management Group (OMG) heeft deze nieuwe standaard uitgebracht met als doel een standaard taal te creëren om 1) requirements voor beslissingen en 2) de beslissingen zelf te modelleren. De adoptie van DMN heeft een wat lange aanloop gehad, maar begint nu serieuze vormen aan te nemen. Om deze reden brengen wij een vierdelige serie over DMN en het gebruik van DMN uit. In deze introductie, deel 1, gaan we in op de basis van The Decision Model and Notation.
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Since an increasing amount of business decision/logic management solutions are utilized, organizations search for guidance to design such solutions. An important aspect of such a solution is the ability to guard the quality of the specified or modified business decisions and underlying business logic to ensure logical soundness. This particular capability is referred to as verification. As an increasing amount of organizations adopt the new Decision Management and Notation (DMN) standard, introduced in September 2015, it is essential that organizations are able to guard the logical soundness of their business decisions and business logic with the help of certain verification capabilities. However, the current knowledge base regarding verification as a capability is not yet researched in relation to the new DMN standard. In this paper, we re-address and - present our earlier work on the identification of 28 verification capabilities applied by the Dutch government [1]. Yet, we extended the previous research with more detailed descriptions of the related literature, findings, and results, which provide a grounded basis from which further, empirical, research on verification capabilities with regards to business decisions and business logic can be explored.
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Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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In 2015, the Object Management Group published the Decision Model and Notation with the goal to structure and connect business processes, decisions and underlying business logic. Practice shows that several vendors adopted the DMN standard and (started to) integrate the standard with their tooling. However, practice also shows that there are vendors who (consciously) deviate from the DMN standard while still trying to achieve the goal DMN is set out to reach. This research aims to 1) analyze and benchmark available tooling and their accompanied languages according to the DMN-standard and 2) understand the different approaches to modeling decisions and underlying business logic of these vendor specific languages. We achieved the above by analyzing secondary data. In total, 22 decision modelling tools together with their languages were analyzed. The results of this study reveal six propositions with regards to the adoption of DMN with regards to the sample of tools. These results could be utilized to improve the tools as well as the DMN standard itself to improve adoption. Possible future research directions comprise the improvement of the generalizability of the results by including more tools available and utilizing different methods for the data collection and analysis as well as deeper analysis into the generation of DMN directly from tool-native languages.
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Proper decision-making is one of the most important capabilities of an organization. Therefore, it is important to have a clear understanding and overview of the decisions an organization makes. A means to understanding and modeling decisions is the Decision Model and Notation (DMN) standard published by the Object Management Group in 2015. In this standard, it is possible to design and specify how a decision should be taken. However, DMN lacks elements to specify the actors that fulfil different roles in the decision-making process as well as not taking into account the autonomy of machines. In this paper, we re-address and-present our earlier work [1] that focuses on the construction of a framework that takes into account different roles in the decision-making process, and also includes the extent of the autonomy when machines are involved in the decision-making processes. Yet, we extended our previous research with more detailed discussion of the related literature, running cases, and results, which provides a grounded basis from which further research on the governance of (semi) automated decision-making can be conducted. The contributions of this paper are twofold; 1) a framework that combines both autonomy and separation of concerns aspects for decision-making in practice while 2) the proposed theory forms a grounded argument to enrich the current DMN standard.
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Decisions are used by organizations to manage and execute their coordinated, value-adding decision-making and are thereby among an organization’s most important assets. To be able to manage deci-sions and underlying business rules, Decision Management (DM) and Business Rules Management (BRM) are increasingly being applied at organisations. One of the latest developments related to the domain of DM and BRM is the introduction of the Decision Model and Notation (DMN) in September 2015 by the Object Management Group (OMG). The goal of this technical paper is to provide students with a case to practice the specification, verification, validation, deployment, execution, monitoring and governance of business rules in practice.
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Background: Given that relapse is common in patients in remission from anxiety and depressive disorders, relapse prevention is needed in the maintenance phase. Although existing psychological relapse prevention interventions have proven to be effective, they are not explicitly based on patients’ preferences. Hence, we developed a blended relapse prevention program based on patients’ preferences, which was delivered in primary care practices by mental health professionals (MHPs). This program comprises contact with MHPs, completion of core and optional online modules (including a relapse prevention plan), and keeping a mood and anxiety diary in which patients can monitor their symptoms. Objective: The aims of this study were to provide insight into (1) usage intensity of the program (over time), (2) the course of symptoms during the 9 months of the study, and (3) the association between usage intensity and the course of symptoms. Methods: The Guided E-healTh for RElapse prevention in Anxiety and Depression (GET READY) program was guided by 54 MHPs working in primary care practices. Patients in remission from anxiety and depressive disorders were included. Demographic and clinical characteristics, including anxiety and depressive symptoms, were collected via questionnaires at baseline and after 3, 6, and 9 months. Log data were collected to assess the usage intensity of the program. Results: A total of 113 patients participated in the study. Twenty-seven patients (23.9%) met the criteria for the minimal usage intensity measure. The core modules were used by ≥70% of the patients, while the optional modules were used by <40% of the patients. Usage decreased quickly over time. Anxiety and depressive symptoms remained stable across the total sample; a minority of 15% (12/79) of patients experienced a relapse in their anxiety symptoms, while 10% (8/79) experienced a relapse in their depressive symptoms. Generalized estimating equations analysis indicated a significant association between more frequent face-to-face contact with the MHPs and an increase in both anxiety symptoms (β=.84, 95% CI .39-1.29) and depressive symptoms (β=1.12, 95% CI 0.45-1.79). Diary entries and the number of completed modules were not significantly associated with the course of symptoms.
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