Treiber der Adoption SaaS-basierter Anwendungen

Software-as-a-Service (SaaS) is said to become an important cornerstone of the Internet of Services. However, while some market research and IT provider firms fervently support this point of view, others already conjure up the failure of this on-demand sourcing option. Oftentimes based on weak empirical data and shaky reasoning, these inconsistent perspectives lack scientific rigor and neglect to present a more differentiated picture of SaaS-adoption. This study seeks to deepen the understanding of factors driving the adoption of Software-as-a-Service (SaaS). Grounded in transaction cost theory, the resource-based view, and the theory of planned behavior, the authors develop a research model to assess SaaS-adoption at the application level. Survey data of 297 firms in Germany with 374 valid response items across different industries were collected to test the theoretical model. Our analysis revealed that patterns of the decision on SaaS-adoption differ across application types. Social influence, attitude toward SaaS-adoption, adoption uncertainty, and strategic value turned out to be the strongest and most consistent drivers across all application types. Furthermore, it was found that firm size does not matter in SaaS-adoption, since large enterprises and small- and medium-sized companies had similar adoptation rates. Overall, this study provides relevant findings that IT vendors can use to better appeal to potential companies that consider adopting SaaS.     

Scaling Kernel-Based Systems to Large Data Sets

In the form of the support vector machine and Gaussian processes, kernel-based systems are currently very popular approaches to supervised learning. Unfortunately, the computational load for training kernel-based systems increases drastically with the size of the training data set, such that these systems are not ideal candidates for applications with large data sets. Nevertheless, research in this direction is very active. In this paper, I review some of the current approaches toward scaling kernel-based systems to large data sets.     

Transforming smoothers for PDE constrained optimization problems

Transforming smoothers are known as a successful approach to the multigrid treatment of saddlepoint problems resulting from variational problems. In this paper we analyze similar multigrid methods in the context of the practically important class of optimization problems with partial differential equation constraint, which lead to a different kind of saddle point problems. We prove level independent convergence properties for the resulting multigrid methods and demonstrate this also in numerical investigations for a relevant model problem.     

Kernel fisher discriminants for outlier detection

The problem of detecting atypical objects or outliers is one of the classical topics in (robust) statistics. Recently, it has been proposed to address this problem by means of one-class SVM classifiers. The method presented in this letter bridges the gap between kernelized one-class classification and gaussian density estimation in the induced feature space. Having established the exact relation between the two concepts, it is now possible to identify atypical objects by quantifying their deviations from the gaussian model. This model-based formalization of outliers overcomes the main conceptual shortcoming of most one-class approaches, which, in a strict sense, are unable to detect outliers, since the expected fraction of outliers has to be specified in advance. In order to overcome the inherent model selection problem of unsupervised kernel methods, a cross-validated likelihood criterion for selecting all free model parameters is applied. Experiments for detecting atypical objects in image databases effectively demonstrate the applicability of the proposed method in real-world scenarios.     

Service level management with agent technology

This paper outlines a generic evaluation methodology for multimedia and real time applications. It concentrates on the application layer and the service aspect. Principles of software evaluation for quality assessment and measurement for networked services and distributed applications are used to present a specific method for measuring important characteristics. The method and the measurement procedure allow comparison of the actual characteristics of service quality with the required characteristics and thresholds. The proof of concepts will be made by the implementation of quality measurement agents following the approach outlined in this paper. Our approach of performing service level management (SLM) with agent technology is distributed, slim, minimizable to the maximum, independent in its methodology and offers comparable, objective results.     

Observations of quantized vorticity generated in superfluid 4He flow through 2-Μm-diameter orifices

A beam of charged vortex rings is used to study the vorticity present downstream from an array of 2-m-diameter orifices through which a steady flow of superfluid ⁴He is driven at known velocity and pressure head p. From the measured attentuation of the beam at low p, we infer a density of vortex line less than predicted by the Feynman critical velocity model. Possible explanations are considered. We find evidence that an interconnected tangle of vortices is formed above a certain value of p, which is in reasonable agreement with theory, at least for one orifice plate. Charge transfer and transient attenuation measurements indicate that this tangle decays in part into small vortex rings. These results are compared with an earlier experiment of Gamota.Research supported in part by the National Science Foundation under grant DMR 72-02971.     

On the number of term orders

By an admissible order on a finite subsetQ of ⁿ we mean the restriction toQ of a linear order on ⁿ compatible with the group structure of ⁿ and such that ⁿ is contained in the positive cone of the order. We first derive upper and lower bounds on the number of admissible orders on a given setQ in terms of the dimensionn and the cardinality ofQ. Better estimates are possible if the setQ enjoys symmetry properties and in the case whereQ is a discrete hyperbox of the form In the latter case, we also give asymptotic results as d _k for fixedn. We finally present algorithms which compute the set of admissible orders that extend a given binary relation onQ and their number. The algorithms are useful in connection with the construction of universal Gröbner bases.AMS Classification: primary 06F20 secondary 06-04, 11N25     

Modeling broad-scale patterns of avian species richness across the Midwestern United States with measures of satellite image texture

Avian biodiversity is threatened, and in order to prioritize limited conservation resources and conduct effective conservation planning a better understanding of avian species richness patterns is needed. The use of image texture measures, as a proxy for the spatial structure of land cover and vegetation, has proven useful in explaining patterns of avian abundance and species richness. However, prior studies that modeled habitat with texture measures were conducted over small geographical extents and typically focused on a single habitat type. Our goal was to evaluate the performance of texture measures over broad spatial extents and across multiple habitat types with varying levels of vertical habitat structure. We calculated a suite of texture measures from 114 Landsat images over a study area of 1,498,000 km² in the Midwestern United States, which included habitats ranging from grassland to forest. Avian species richness was modeled for several functional guilds as a function of image texture. We subsequently compared the explanatory power of texture-only models with models fitted using landscape composition metrics derived from the National Land Cover Dataset, as well as models fitted using both texture and composition metrics. Measures of image texture were effective in modeling spatial patterns of avian species richness in multiple habitat types, explaining up to 51% of the variability in species richness of permanent resident birds. In comparison, landscape composition metrics explained up to 56% of the variability in permanent resident species richness. In the most heavily forested ecoregion, texture-measures outperformed landscape metrics, and the two types of measurements were complementary in multivariate models. However, in two out of three ecoregions examined, landscape composition metrics consistently performed slightly better than texture measures, and the variance explained by the two types of measures overlapped considerably. These results show that image texture measures derived from satellite imagery can be an important tool for modeling patterns of avian species richness at broad spatial extents, and thus assist in conservation planning. However, texture measures were slightly inferior to landscape composition metrics in about three-fourths of our models. Therefore texture measures are best considered in conjunction with landscape metrics (if available) and are best used when they show explanatory ability that is complementarity to landscape metrics.