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This essay represents a pioneering attempt to conceive of Mies van der Rohe's work as a transformation of ‘archetypes’. We will begin by addressing Mies' belief in architectural objectivity, which rests on three theoretic positions: first, the antispeculative reference to scientific practice of Alois Riehl‘s philosophy; secondly, the ‘objective philosophy’ of the biologist Raoul H. Francé, which requires that any effective action follows the “laws of the renewal of the organic world”; and finally, the demand for architectural renewal through the transformation of archetypal forms made by Hendrik Petrus Berlage following Gottfried Semper. It is notably Mies' reference to Berlage's theorem that will be explored in the second part of this essay that will shed new light onto his design practice. At the core of the analysis an array of specific designs by Frank Lloyd Wright provides the framework for a fundamental understanding of Mies's early country house designs as resting on the process of modernization of archetypes; these were embodied in Japanese interiors, reproductions of which were widely disseminated in Germany in the 1920s, as well as in the then re-discovered work of Schinkel’s disciple Ludwig Persius. The aim of this study of renewal through transformation is not only to reveal Mies' design process as a matter of complex visual ‘calculus’, but also to make a general contribution to the semantics of modernity.

This article describes how ten exemplary urban school principals worked together under a Wallace Foundation Grant to advance the understanding of urban school leadership. The grant's intent was to contribute to the development of a national model for the assessment of master principals by demonstrating how building-level leadership in urban schools can significantly improve student academic performance. Principals were required to submit portfolios reflecting their contributions. Instead, these principals submitted a co-authored book revealing their study of the nature of "urban" and highlighting their individual case stories. In so doing, a unique professional learning community emerged that spanned across urban schools and school districts.

Our analytics challenge is is to identify, characterize, and visualize anomalous subsets of large collections of network connection data. We use a combination of HPC resources, advanced algorithms, and visualization techniques. To effectively and efficiently identify the salient portions of the data, we rely on a multi-stage workflow that includes data acquisition, summarization (feature extraction), novelty detection, and classification. Once these subsets of interest have been identified and automatically characterized, we use a state-of-the-art high-dimensional query system to extract data subsets for interactive visualization. Our approach is equally useful for other large-data analysis problems where it is more practical to identify interesting subsets of the data for visualization than to render all data elements. By reducing the size of the rendering workload, we enable highly interactive and useful visualizations. As a result of this work we were able to analyze six months worth of data interactively with response times two orders of magnitude shorter than with conventional methods.