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- 저자 : Tilo (검색결과 4 건)
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Equivalence Heuristics for Malleability-Aware Skylines
Christoph Lofi,Wolf-Tilo Balke,Ulrich Güntzer 한국정보과학회 2012 Journal of Computing Science and Engineering Vol.6 No.3
In recent years, the skyline query paradigm has been established as a reliable method for database query personalization. While early efficiency problems have been solved by sophisticated algorithms and advanced indexing, new challenges in skyline retrieval effectiveness continuously arise. In particular, the rise of the Semantic Web and linked open data leads to personalization issues where skyline queries cannot be applied easily. We addressed the special challenges presented by linked open data in previous work; and now further extend this work, with a heuristic workflow to boost efficiency. This is necessary; because the new view on linked open data dominance has serious implications for the efficiency of the actual skyline computation, since transitivity of the dominance relationships is no longer granted. Therefore, our contributions in this paper can be summarized as: we present an intuitive skyline query paradigm to deal with linked open data; we provide an effective dominance definition, and establish its theoretical properties; we develop innovative skyline algorithms to deal with the resulting challenges; and we design efficient heuristics for the case of predicate equivalences that may often happen in linked open data. We extensively evaluate our new algorithms with respect to performance, and the enriched skyline semantics.
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Equivalence Heuristics for Malleability-Aware Skylines
Lofi, Christoph,Balke, Wolf-Tilo,Guntzer, Ulrich Korean Institute of Information Scientists and Eng 2012 Journal of Computing Science and Engineering Vol.6 No.3
In recent years, the skyline query paradigm has been established as a reliable method for database query personalization. While early efficiency problems have been solved by sophisticated algorithms and advanced indexing, new challenges in skyline retrieval effectiveness continuously arise. In particular, the rise of the Semantic Web and linked open data leads to personalization issues where skyline queries cannot be applied easily. We addressed the special challenges presented by linked open data in previous work; and now further extend this work, with a heuristic workflow to boost efficiency. This is necessary; because the new view on linked open data dominance has serious implications for the efficiency of the actual skyline computation, since transitivity of the dominance relationships is no longer granted. Therefore, our contributions in this paper can be summarized as: we present an intuitive skyline query paradigm to deal with linked open data; we provide an effective dominance definition, and establish its theoretical properties; we develop innovative skyline algorithms to deal with the resulting challenges; and we design efficient heuristics for the case of predicate equivalences that may often happen in linked open data. We extensively evaluate our new algorithms with respect to performance, and the enriched skyline semantics.
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Balloon-Supported Passage of a Stent-Graft into the Aortic Arch
은나래,이다혜,송석원,주승문,Tilo Kölbel,이광훈 대한영상의학회 2015 Korean Journal of Radiology Vol.16 No.4
A 62-year-old man was admitted, and thoracic endovascular aortic repair (TEVAR) procedure was performed to treat an accidentally detected aortic aneurysm, which was 63 mm in diameter. While performing TEVAR, the passage of the stentgraft introducer system was impossible due to the prolapse of the introducer system into a wide-necked aneurysm; this aneurysm was located at the greater curvature of the proximal descending thoracic aorta. In order to advance the introducer system, a compliant balloon was inflated. Thus, we created an artificial wall in the aneurysm with this inflated balloon. Finally, we were able to advance the introducer system into the target zone.
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Weikert Thomas,Rapaka Saikiran,Grbic Sasa,Re Thomas,Chaganti Shikha,Winkel David J.,Anastasopoulos Constantin,Niemann Tilo,Wiggli Benedikt J.,Bremerich Jens,Twerenbold Raphael,Sommer Gregor,Comaniciu 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.6
Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients’ needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.
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