New Avenues for Theoretical Contributions in Enterprise Architecture Principles - A Literature Review

Enterprise Architecture (EA), which has been approached by both academia and industry, is considered comprising not only architectural representations, but also principles guiding architecture's design and evolution. Even though the concept of EA principles has been defined as the integral part of EA, the number of publications on this subject is very limited and only a few organizations use EA principles to manage their EA endeavors. In order to critically assess the current state of research and identify research gaps in EA principles, we focus on four general aspects of theoretical contributions in IS. By applying these aspects to EA principles, we outline future research directions in EA principles nature, adoption, practices, and impact.

Opposite impacts of dietary versus supplemental calcium on cardiovascular health.

Commentary on: Li K, Kaaks R, Linseisen J, et al . Associations of dietary calcium intake and calcium supplementation with myocardial infarction and stroke risk and overall cardiovascular mortality in the Heidelberg cohort of the European prospective investigation into cancer and nutrition study (EPIC-Heidelberg). Heart 2012; 98 :920 - 5

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

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Religious diversity is often captured in "mapping studies" that use mostly qualitative methods in order to map and assess the religious communities in a given area. While these studies are useful, they often present weaknesses in that they treat only limited geographic regions, give limited possibilities of comparing across religious groups and cannot test theories. In this chapter, we show how a census and a quantitative National Congregations Study (NCS) methodology can be combined in order to map and assess the religious diversity of a whole country (Switzerland), overcoming the problems mentioned above. We show the methodological steps and selected results concerning organizational, geographic, structural, and cultural diversity.

Large-scale clustering through functional embedding

We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization approach, does not require a matrix inversion, can easily encode prior knowledge in the set of implementable functions, and does not have an ?out-of-sample? problem. Experimental results on both artificial and real-world datasets show the usefulness of our approach.