Abstract-Developing applications, especially real-time ones, for wireless vehicular ad hoc networks (VANETs) requires a reasonable assurance of the likely performance of the network, at the least in terms of packet loss ratios and end-to-end delay. Because wireless propagation strongly influences performance, especially in an urban environment, this paper improves on simpler propagation models for simulations by augmenting ray-tracing derived models of propagation. In the non-line-of-sight component: the propagation distance is more closely calculated according to the reflection distance; the effect of roadside obstacles is included; and for modeling of fast fading a phase factor is introduced, all without necessarily overly increasing computational load. In the line-of-sight component, as well as roadside obstacle modeling: single and double reflections from roadside buildings are added to the standard two-ray ground-propagation model; the distribution of vehicles within a street segment is used to model the ground reflection ray more closely; and the reflection coefficient is also adjusted accordingly to account for reflections from vehicles. The results have been compared with widely-used measurement studies of city streets in the literature, which have confirmed the overall advantage of the improvements, especially in the case of the non-line-of-sight component. A simulation case study shows that in general optimistic performance predictions of packet loss occur with the two-ray ground propagation model when indiscriminately applied.The paper, therefore, represents a way forward for VANET wireless channel modeling in simulations.
Trace clustering has increasingly been applied to find homogenous process executions. However, current techniques have difficulties in finding a meaningful and insightful clustering of patients on the basis of healthcare data. The resulting clusters are often not in line with those of medical experts, nor do the clusters guarantee to help return meaningful process maps of patients' clinical pathways. After all, a single hospital may conduct thousands of distinct activities and generate millions of events per year. In this paper, we propose a novel trace clustering approach by using sample sets of patients provided by medical experts. More specifically, we learn frequent sequence patterns on a sample set, rank each patient based on the patterns, and use an automated approach to determine the corresponding cluster. We find each cluster separately, while the frequent sequence patterns are used to discover a process map. The approach is implemented in ProM and evaluated using a large data set obtained from a university medical center. The evaluation shows F1-scores of 0.7 for grouping kidney injury, 0.9 for diabetes, and 0.64 for head/neck tumor, while the process maps show meaningful behavioral patterns of the clinical pathways of these groups, according to the domain experts.
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Grouping patients meaningfully can give insights about the different types of patients, their needs, and the priorities. Finding groups that are meaningful is however very challenging as background knowledge is often required to determine what a useful grouping is. In this paper we propose an approach that is able to find groups of patients based on a small sample of positive examples given by a domain expert. Because of that, the approach relies on very limited efforts by the domain experts. The approach groups based on the activities and diagnostic/billing codes within health pathways of patients. To define such a grouping based on the sample of patients efficiently, frequent patterns of activities are discovered and used to measure the similarity between the care pathways of other patients to the patients in the sample group. This approach results in an insightful definition of the group. The proposed approach is evaluated using several datasets obtained from a large university medical center. The evaluation shows F1-scores of around 0.7 for grouping kidney injury and around 0.6 for diabetes.
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