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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Process Mining can roughly be defined as a data-driven approach to process management. The basic idea of process mining is to automatically distill and to visualize business processes using event logs from company IT-systems (e.g. ERP, WMS, CRM etc.) to identify specific areas for improvement at an operational level. An event log can be described as a database entry that signifies a specific action in a software application at a specific time. Simple examples of these actions are customer order entries, scanning an item in a warehouse, and registration of a patient for a hospital check-up.Process mining has gained popularity in the logistics domain in recent years because of three main reasons. Firstly, the logistics IT-systems' large and exponentially growing amounts of event data are being stored and provide detailed information on the history of logistics processes. Secondly, to outperform competitors, most organizations are searching for (new) ways to improve their logistics processes such as reducing costs and lead time. Thirdly, since the 1970s, the power of computers has grown at an astonishing rate. As such, the use of advance algorithms for business purposes, which requires a certain amount of computational power, have become more accessible.Before diving into Process Mining, this course will first discuss some basic concepts, theories, and methods regarding the visualization and improvement of business processes.
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Uit de aankondiging: "Steeds meer systemen loggen gegevens over hoe het bedrijfsproces verloopt, maar loopt het proces wel zoals het bedoeld was? Wat zijn de knelpunten? Text mining is vaak lastig doordat er tijdstippen ontbreken, process mining kan niet werken zonder tijdstippen, de combinatie van die twee technieken kan elkaar versterken. Bij sentiment mining weet je wel wat iemands zijn gevoelens zijn, maar niet zijn drijfveren, terwijl drijfveren juist een betere verklaring voor iemands gedrag vormen. De combinatie van deze technieken biedt mogelijkheden om nieuwe inzichten te verwerven rond customer journeys, zodat de klant uiteindelijk beter geholpen wordt." http://www.naf.nl/events/proces-text-mining/
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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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This method paper presents a template solution for text mining of scientific literature using the R tm package. Literature to be analyzed can be collected manually or automatically using the code provided with this paper. Once the literature is collected, the three steps for conducting text mining can be performed as outlined below:• loading and cleaning of text from articles,• processing, statistical analysis, and clustering, and• presentation of results using generalized and tailor-made visualizations.The text mining steps can be applied to a single, multiple, or time series groups of documents.References are provided to three published peer reviewed articles that use the presented text mining methodology. The main advantages of our method are: (1) Its suitability for both research and educational purposes, (2) Compliance with the Findable Accessible Interoperable and Reproducible (FAIR) principles, and (3) code and example data are made available on GitHub under the open-source Apache V2 license.
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From the article: Within the HU University of Applied Sciences (HU) the department HU Services (HUS) has not got enough insight in their IT Service Management processes to align them to the new Information System that is implemented to support the service management function. The problem that rises from this is that it is not clear for the HU how the actual Incident Management process as facilitated by the application is actually executed. Subsequently it is not clear what adjustments have to be made to the process descriptions to have it resemble the process in the IT Service Management tool. To determine the actual process the HU wants to use Process Mining. Therefore the research question for this study is: ‘How is Process Mining applicable to determine the actual Incident Management process and align this to the existing process model descriptions?’ For this research a case study is performed using Process Mining to check if the actual process resembles like the predefined process. The findings show that it is not possible to mine the process within the scope of the predefined process. The event data are too limited in granularity. From this we conclude that adjustment of the granularity of the given process model to the granularity of the used event data or vice versa is important.
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Corporate Social Responsibility (CSR) has become an important concern in the mining sector in recent years but has been overlooked heavily in the context of developing countries. This article helps to bridge this gap by exploring management and stakeholders' perceptions of a Malawian-based Australian multinational mining company's CSR strategy. The findings suggest that management's views of CSR differ significantly from those of stakeholders. While managers have a classical and limited view of the firm's role in mining communities and wider society, stakeholders generally have a broader idea of what social responsibilities companies can assume within wider society.
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While there has been a growing body of research focused on corporate social responsibility (CSR) practices in developing economies, few studies have examined the factors shaping the CSR agenda in sub-Saharan countries. Using qualitative data obtained through semi-structured interviews with management and stakeholders, this paper examines the drivers of the CSR agenda pursued by Paladin (Africa), a subsidiary of an Australian multinational mining company (MNC) operating the first uranium mine in Malawi. The findings suggest that the CSR agenda in the mining industry in Malawi is strongly influenced by externally generated pressures such as civil society organisation activism and community expectations; although it is clear that other drivers such as public and private regulations and pressure from financial markets also played a role in pressurising Paladin to adopt a CSR agenda.
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In recent years, stakeholder engagement has increasingly become a catchphrase in response to calls for corporate accountability to their stakeholders in the developing countries. However, the processes and practices companies pursue to engage stakeholders tend to conspicuously be variable depending on whether one draws on the instrumental and descriptive perspectives of the stakeholder theory. The purpose of this paper is therefore to test these perspectives, which we do through considering the case of a subsidiary of a multinational firm fictitiously known as Ashford (Africa) Limited, which operates in Malawi, as a member of the global mining industry. Using qualitative data obtained from interviews with Ashford (Malawi)'s managers and stakeholders, this study highlights the significance of paying more attention to firm specific factors, community dynamics and the civil society (NGO) related factors, as they are fundamental to the effectiveness of stakeholder engagement agenda pursued by mining companies in the developing countries.
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handout van een labtalk waarin de onderzoeker enkele methoden beschrijft rond text mining, story mining: het herkennen van patronen in communicatie met klanten.
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