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Auflistung Künstliche Intelligenz 38(3) - November 2024 nach Schlagwort "Clustering"
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- ZeitschriftenartikelAnalyzing Semantically Enriched Trajectories(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Seep, JanaIn order to understand what influences the movement of an object or person it is important to consider a variety of factors. These could be the visibility of certain landmarks, the current temperature or the presence of a crowded area to be avoided. These insights then can be used to understand movement in the public sector and improve our build environment, e.g. to reduce street traffic accidents or orientation in complex buildings. The following extended abstract is a summary of a doctoral thesis submitted to the University of Münster. The thesis was successfully defended in February 2023 [ 16 ]. The dissertation focuses on the analysis of so-called semantically enriched trajectories , which are used to describe observed movement. It proposes a new model based on an extended finite state machine, which allows for the representation and consideration of the information about the context of the trajectory. With the new model, we consider two main steps in trajectory analysis: First, we aim to infer a semantically enriched representative trajectory for a given cluster of trajectories. Second, we introduce a variation of the well-known k-means algorithm to calculate clusters based on the given context of trajectories. To show semantic feasibility of our approach, we conclude this work by evaluating the possibility to provide decision support for domain experts in two different public sector related contexts.
- ZeitschriftenartikelPrivAgE: A Toolchain for Privacy-Preserving Distributed Aggregation on Edge-Devices(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Liebenow, Johannes; Imort, Timothy; Fuchs, Yannick; Heisel, Marcel; Käding, Nadja; Rupp, Jan; Mohammadi, EsfandiarValuable insights, such as frequently visited environments in the wake of the COVID-19 pandemic, can oftentimes only be gained by analyzing sensitive data spread across edge-devices like smartphones. To facilitate such an analysis, we present a toolchain called PrivAgE for a distributed, privacy-preserving aggregation of local data by taking the limited resources of edge-devices into account. The distributed aggregation is based on secure summation and simultaneously satisfies the notion of differential privacy. In this way, other parties can neither learn the sensitive data of single clients nor a single client’s influence on the final result. We perform an evaluation of the power consumption, the running time and the bandwidth overhead on real as well as simulated devices and demonstrate the flexibility of our toolchain by presenting an extension of the summation of histograms to distributed clustering.