A large amount of data accommodated in knowledge graphs (KG) is metric. For example, the Wikidata KG contains a plenitude of metric facts about geographic entities like cities or celestial objects. In this paper, we propose a novel approach that transfers orometric (topographic) measures to bounded metric spaces. While these methods were originally designed to identify relevant mountain peaks on the surface of the earth, we demonstrate a notion to use them for metric data sets in general. Notably, metric sets of items enclosed in knowledge graphs. Based on this we present a method for identifying outstanding items using the transferred valuations functions isolation and prominence. Building up on this we imagine an item recommendation process. To demonstrate the relevance of the valuations for such processes, we evaluate the usefulness of isolation and prominence empirically in a machine learning setting. In particular, we find structurally relevant items in the geographic population distributions of Germany and France.

For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. Different measures are proposed in the literature for determining the similarity of these likelihoods. They are usually evaluated in studies with specific settings. In this work we compare, in a daily-life setting, various measures of distance between such likelihoods in combination with different methods for estimation and representation. In particular, we show that among the considered distance measures the Earth Mover's Distance is the most beneficial for the localization task.

This paper focuses on the analysis of socio-spatial data, i. e., user-performance relations at a distributed event. We consider the data as a bimodal network (i. e., model it as a bipartite graph), and investigate its structural characteristics towards a social network. We focus on plans of the participants (expressed by preferences) and their fulfilment, and propose measures for matching preference and reality. We specifically analyse behavioural patterns w.r.t. distinct user and performance groups. We utilise real-world data collected at the Lange Nacht der Musik (Long Night of Music) 2013 in Munich.