The value of a decision can be increased through analyzing the decision logic, and the outcomes. The more often a decision is taken, the more data becomes available about the results. More available data results into smarter decisions and increases the value the decision has for an organization. The research field addressing this problem is Decision mining. By conducting a literature study on the current state of Decision mining, we aim to discover the research gaps and where Decision mining can be improved upon. Our findings show that the concepts used in the Decision mining field and related fields are ambiguous and show overlap. Future research directions are discovered to increase the quality and maturity of Decision mining research. This could be achieved by focusing more on Decision mining research, a change is needed from a business process Decision mining approach to a decision focused approach.
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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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The European Arctic has been recently experiencing an upsurge in mining activities. This is reflected in an on-going interest from the industry, regulators and the public. However, current and future prospects are highly sensitive to mineral price fluctuations. The EU is a major consumer and importer of Arctic raw materials. As the EU is concerned about the security of supply, it attempts to encourage domestic mineral extraction.Both Arctic communities and industry call for enhanced information flows, as well as improved and more inclusive decision-making frameworks. The EU should clearly articulate its interests related to mining in the European Arctic. The EU could further enhance its support for the collection and sharing of mining data and knowledge.The EU regulatory framework could better contribute to harmonising environmental, economic and social assessments, paying special attention to local social issues and indigenous rights. The EU, as a major global actor, can also influence international governance, standard-setting and co-operation to facilitate increased responsibility in mining activities, including through dialogue with mining industry.
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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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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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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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During the past two decades the implementation and adoption of information technology has rapidly increased. As a consequence the way businesses operate has changed dramatically. For example, the amount of data has grown exponentially. Companies are looking for ways to use this data to add value to their business. This has implications for the manner in which (financial) governance needs to be organized. The main purpose of this study is to obtain insight in the changing role of controllers in order to add value to the business by means of data analytics. To answer the research question a literature study was performed to establish a theoretical foundation concerning data analytics and its potential use. Second, nineteen interviews were conducted with controllers, data scientists and academics in the financial domain. Thirdly, a focus group with experts was organized in which additional data were gathered. Based on the literature study and the participants responses it is clear that the challenge of the data explosion consist of converting data into information, knowledge and meaningful insights to support decision-making processes. Performing data analyses enables the controller to support rational decision making to complement the intuitive decision making by (senior) management. In this way, the controller has the opportunity to be in the lead of the information provision within an organization. However, controllers need to have more advanced data science and statistic competences to be able to provide management with effective analysis. Specifically, we found that an important skill regarding statistics is the visualization and communication of statistical analysis. This is needed for controllers in order to grow in their role as business partner..
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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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Designing and personalising systems for specific user groups encompasses a lot of effort with respect to analysing and understanding user behaviour. The goal of our paper is to provide a new methodology for determining navigational patterns of behaviour of specific user groups. We consider agricultural users as a specific user group, during the usage of a decision support system supporting cultivar selection - OPTIRas(TM). Combining process mining techniques with insights from decision making theories, we provide a method of analysing logs resulted from usage of decision support systems. For instance, farmers show difficulties in fulfilling the goal of OPTIRas, while other agricultural users seems to manage better. The results of our analysis can be used to support the redesign and personalization of decision support systems.
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From the article: Abstract Since decision management is becoming an integrated part of business process management, more and more decision management implementations are realized. Therefore, organizations search for guidance to design such solutions. Principles are often applied to guide the design of information systems in general. A particular area of interest when designing decision management solutions is compliance. In an earlier published study (Zoet & Smit, 2016) we took a general perspective on principles regarding the design of decision management solutions. In this paper, we re-address our earlier work, yet from a different perspective, the compliance perspective. Thus, we analyzed how the principles can be utilized in the design of compliant decision management solutions. Therefore, the purpose of this paper is to specify, classify, and validate compliance principles. To identify relevant compliance principles, we conducted a three round focus group and three round Delphi Study which led to the identification of eleven compliance principles. These eleven principles can be clustered into four categories: 1) surface structure principles, 2) deep structure principles, 3) organizational structure principles, and 4) physical structure principles. The identified compliance principles provide a framework to take into account when designing information systems, taking into account the risk management and compliance perspective.
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