This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point clouds are reviewed. This includes the data structures, but also spatial processing concepts. We suggest a categorization of features into levels that reflect the amount of processing. Point clouds are found across many disciplines, which is reflected in the versatility of the literature suggesting specific features.

We formulate the service composition problem as a multi-objective stochastic program which simultaneously optimizes the following quality of service (QoS) parameters: workflow duration, service invocation costs, availability, and reliability. All of these quality measures are modelled as decision-dependent random variables. Our model minimizes the average value-at- risk (AVaR) of the workflow duration and costs while imposing constraints on the workflow availability and reliability. AVaR is a popular risk measure in decision theory which quantifies the expected shortfall below some percentile of a loss distribution. By replacing the random durations and costs with their expected values, our risk-aware model reduces to the nominal problem formulation prevalent in literature. We argue that this nominal model can lead to overly risky decisions. Finally, we report on the scalability properties of our model.

In this article, we present a representation of value-at-risk (VaR) as a difference of convex (D.C.) functions in the case where the distribution of the underlying random variable is discrete and has finitely many atoms. The D.C. representation is used to study a financial risk-return portfolio selection problem with a VaR constraint. A branch-and-bound algorithm that numerically solves the problem exactly is given. Numerical experiments with historical asset returns from representative market indices are performed to apply the algorithm to real-world financial market data.

In our current economic climate of scarce resources there is a lot of debate around the best - and most efficient - way of delivering care, which points patients towards the right physician at the earliest opportunity. The aim of the study was to assess whether an improvement in the interdisciplinary management of rheumatoid arthritis (RA) has the potential to simultaneously improve health outcomes and reduce costs. In a first step, we modelled the ways which lead patients with RA to the correct diagnosis, and the relevant specialist, respectively. On average, a patient experiences 3 GP visits before referral to a specialist. We compared this situation against a reconfiguration of current practice towards a more proactive identification and referral method with initiation of care by a rheumatologist early in the disease. We evaluated the impact of this reconfiguration on the number of RA patients diagnosed and the costs associated with the diagnostic process. Using data on epidemiology and Austrian practice patterns, we estimate a total of 5294 people with undifferentiated arthritis per year, of which 1765 suffer from RA. Modelling for diagnostic accuracy, we found that 1200 of these patients are initially misdiagnosed in a primary care setting and 95 at a rheumatologist. Our model found that a reconfiguration of current practice towards an approach of more integrated care has the potential to be not only cost-effective, but cost-saving: EUR 100,188 could be saved annually by exclusively adopting the new approach. Our results show that by better directing the flow of people with RA, simultaneous clinical and economic benefits may be reaped:.