Eighteenth century style dining area; Steinharter residence; Munich Architecture; Residences; Interior

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Eighteenth century style salon; Steinharter residence; Munich Architecture; Residences; Interior

Digital Image

Eighteenth century style salon; Steinharter residence; Munich Architecture; Residences; Interior

Digital Image

Eighteenth century style salon; Steinharter residence; Munich Architecture; Residences; Interior

Digital Image

Steinharter residence; Munich Architecture; Residences; Interior

Digital Image

Steinharter residence; Munich Architecture; Residences; Interior

Digital Image

Steinharter residence; Munich Architecture; Residences; Interior

Digital Image

Steinharter residence; Munich Architecture; Residences; Interior

Digital Image

Ornate bedroom with mural ceiling; Steinharter residence; Munich Architecture; Residences; Interior

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Kitchen; Steinharter residence; Munich Architecture; Residences; Interior

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Photograph of oil painting portrait of Samuel Loewen; Berlin Portraits Men

circa 1747 in Potsdam - 30 November 1827 in Berlin; father of Rosa Valentin

The Kottek family - The Auerbach family.

2 booklets containing genealogies of the Auerbach and Kottek families, covering circa 1497-1983. Also included are reproductions of family photographs.

Meltdown - a tool for classification and analysis of DSF data

An application that translates raw thermal melt curve data into more easily assimilated knowledge is described. This program, called ‘Meltdown’, performs a number of data remediation steps before classifying melt curves and estimating melting temperatures. The final output is a report that summarizes the results of a differential scanning fluorimetry experiment. Meltdown uses a Bayesian classification scheme, enabling reproducible identification of various trends commonly found in DSF datasets. The goal of Meltdown is not to replace human analysis of the raw data, but to provide a sensible interpretation of the data to make this useful experimental technique accessible to naïve users, as well as providing a starting point for detailed analyses by more experienced users.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.

Detecting drift from event streams of unpredictable business processes

Existing business process drift detection methods do not work with event streams. As such, they are designed to detect inter-trace drifts only, i.e. drifts that occur between complete process executions (traces), as recorded in event logs. However, process drift may also occur during the execution of a process, and may impact ongoing executions. Existing methods either do not detect such intra-trace drifts, or detect them with a long delay. Moreover, they do not perform well with unpredictable processes, i.e. processes whose logs exhibit a high number of distinct executions to the total number of executions. We address these two issues by proposing a fully automated and scalable method for online detection of process drift from event streams. We perform statistical tests over distributions of behavioral relations between events, as observed in two adjacent windows of adaptive size, sliding along with the stream. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in the detection of typical change patterns, and performs significantly better than the state of the art.