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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Recent years have seen a massive growth in ethical and legal frameworks to govern data science practices. Yet one of the core questions associated with ethical and legal frameworks is the extent to which they are implemented in practice. A particularly interesting case in this context comes to public officials, for whom higher standards typically exist. We are thus trying to understand how ethical and legal frameworks influence the everyday practices on data and algorithms of public sector data professionals. The following paper looks at two cases: public sector data professionals (1) at municipalities in the Netherlands and (2) at the Netherlands Police. We compare these two cases based on an analytical research framework we develop in this article to help understanding of everyday professional practices. We conclude that there is a wide gap between legal and ethical governance rules and the everyday practices.
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Managed realignment is the landward relocation of flood infrastructure to re-establish tidal exchange on formerly reclaimed land. Managed realignment can be seen as a nature-based flood defence system that combines flood protection by the realigned dike (artificial) and restored saltmarshes (nature-based). So far, research on coastal managed realignment is primarily directed to saltmarsh restoration on formerly reclaimed land. This study focuses on the realigned dikes. The aim of this research is to characterize realigned dikes and to indicate the characteristics that offer opportunities for nature-based flood protection. We categorized 90 European coastal managed realignment projects into two realigned dike groups: (1) Newly built landward dikes and (2) Existing landward dikes of former multiple dike systems. The second group has two subcategories: (2a) Former hinterland dikes and (2b) Realignments within summer polders. For each group we present the realigned dike characteristics of a representative case study. We consider that the use of existing landward dikes or local construction material make realignment more sustainable. From a nature-based flood protection perspective, the presence of an artificial dike is ambiguous. Our results show that targeted and expected saltmarsh restoration at managed realignment does not necessarily result in a greener realigned dike design that suits for combined flood protection with restored saltmarshes. We recommend coastal managers to explicitly take combined flood protection into account in the realigned dike design and steer the topography of the realignment site to facilitate nature-based flood protection and promote surface elevation increase seaward of the realigned dike in response to sea level rise. This makes managed realignment a nature-based flood defence zone for now and for the future.
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Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
Circular agriculture is an excellent principle, but much work needs to be done before it can become common practice in the equine sector. In the Netherlands, diversification in this sector is growing, and the professional equine field is facing increasing pressure to demonstrate environmentally sound horse feeding management practices and horse owners are becoming more aware of the need to manage their horses and the land on which they live in a sustainable manner. Horses should be provided with a predominantly fibre-based diet in order to mimic their natural feeding pattern, however grazing impacts pasture differently, with a risk of overgrazing and soil erosion in equine pastures. Additionally, most horses receive supplements not only with concentrates and oils, but also with minerals. Though the excess minerals are excreted in the manure of horses, these minerals can accumulate in the soil or leach to nearby waterways and pollute water resources. Therefore, the postdoc research aims to answer the main question, “What horse feeding practices and measurements are needed to reduce and prevent environmental pollution in the Netherlands?” The postdoc research is composed of two components; a broad survey-based study which will generate quantitative data on horse feeding management and will also obtain qualitative data on the owners’ engagement or willingness of horse owners to act sustainably. Secondly, a field study will involve the collection of detailed data via visits to horse stables in order to gather data for nutritional analysis and to collect fecal samples for mineral analysis. Students, lecturers and partners will actively participate in all phases of the planned research. This postdoc research facilitates learning and intends to develop a footprint calculator for sustainable horse feeding to encompass the complexity of the equine sector, and to improve the Equine Sports and Business curriculum.
