arXiv subjects

Leena Ruha

Publications and source records attributed to Leena Ruha.

3 recordsLinked to original sources

arXiv2022

Dominant-feature identification in data from Gaussian processes applied to Finnish forest inventory records

In spatial data, location-dependent variation leads to connected structures known as features. Variations occur at different spatial scales and possibly originate from distinct underlying processes. Each of these scales is characterized by its own dominant features. Here we introduce a statistical method for identifying these scales and their dominant features in data from Gaussian processes. This identification involves credibly recognizing the dominant features by scale-space decomposition and assessing feature attributes by estimating covariance function parameters of the underlying processes and their associations to potential drivers. We analyze Finnish forest inventory data from the 1920s using this dominant-feature identification method and identify the scales of variation in basal area estimates of most common Finnish trees, including Scots pine, Norway spruce, birch, and other native deciduous trees. Comparing the resulting scale-dependent features and their attributes in these tree species, we identify the different effects of edaphic and anthropogenic drivers on the spatial distribution of their basal areas. These data are analyzed for the first time in terms of their scale of variation, and the resulting scale-dependent maps and estimates are an essential contribution to the historical forest ecology of Fennoscandia. Until now, this analysis was not possible with conventional methods.

stat.ME↗

arXiv2021

Edge computing server placement with capacitated location allocation

The deployment of edge computing infrastructure requires a careful placement of the edge servers, with an aim to improve application latencies and reduce data transfer load in opportunistic Internet of Things systems. In the edge server placement, it is important to consider computing capacity, available deployment budget, and hardware requirements for the edge servers and the underlying backbone network topology. In this paper, we thoroughly survey the existing literature in edge server placement, identify gaps and present an extensive set of parameters to be considered. We then develop a novel algorithm, called PACK, for server placement as a capacitated location-allocation problem. PACK minimizes the distances between servers and their associated access points, while taking into account capacity constraints for load balancing and enabling workload sharing between servers. Moreover, PACK considers practical issues such as prioritized locations and reliability. We evaluate the algorithm in two distinct scenarios: one with high capacity servers for edge computing in general, and one with low capacity servers for Fog computing. Evaluations are performed with a data set collected in a real-world network, consisting of both dense and sparse deployments of access points across a city area. The resulting algorithm and related tools are publicly available as open source software.

cs.NI↗

arXiv2021

Capacitated spatial clustering with multiple constraints and attributes

Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing, classifying, logistic optimization and infrastructure optimization. Depending on the application at hand, a wide set of extensions may be necessary in clustering. In this article we propose a number of novel extensions to PACK that is a novel capacitated spatial clustering method. These extensions are relocation and location preference of cluster centers, outliers, and non-spatial attributes. The strength of PACK is that it can consider all of these extensions jointly. We demonstrate the usefulness PACK with a real world example in edge computing server placement for a city region with various different set ups, where we take into consideration outliers, center placement, and non-spatial attributes. Different setups are evaluated with summary statistics on spatial proximity and attribute similarity. As a result, the similarity of the clusters was improved at best by 53%, while simultaneously the proximity degraded only 18%. In alternate scenarios, both proximity and similarity were improved. The different extensions proved to provide a valuable way to include non-spatial information into the cluster analysis, and attain better overall proximity and similarity. Furthermore, we provide easy-to-use software tools (rpack) for conducting clustering analyses.

stat.AP↗