Enterprise Integration Patterns (EIP) are a collection of widely used patterns for integrating enterprise applications and business processes. These patterns represent a “de-facto” standard for design decisions when integrating enterprise applications. For the specification of integration scenarios, the patterns’ control and data flow syntax and semantics have been expressed in the Business Process Model and Notation (BPMN). However, exceptions during message processing are left for further studies. In previous work, we specified common technical, exceptional situations in integration systems and derive exception types, for which we define a compliant representation in BPMN, resulting in general patterns for exception handling and compensation. In addition to the patterns, the Exception Flow was introduced, evaluated syntactically and semantically for representative integration scenarios. In this work, we complement these contributions by extending the exception strategies and patterns, and by adding an evaluation of pattern-based compilation from BPMN-based integration descriptions to an open source integration runtime system.

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Electroencephalogram data used in the domain of brain–computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this information is provided by the feature covariance matrix its large number of free parameters calls for regularization approaches like Ledoit–Wolf shrinkage. Assuming that the noise of event-related potential recordings is not time-locked, we propose to decouple the time component from the covariance matrix of event-related potential data in order to further improve the estimates of the covariance matrix for linear discriminant analysis. We compare three regularized variants thereof and a feature representation based on Riemannian geometry against our proposed novel linear discriminant analysis with time-decoupled covariance estimates. Extensive evaluations on 14 electroencephalogram datasets reveal, that the novel approach increases the classification performance by up to four percentage points for small training datasets, and gracefully converges to the performance of standard shrinkage-regularized LDA for large training datasets. Given these results, practitioners in this field should consider using our proposed time-decoupled covariance estimation when they apply linear discriminant analysis to classify event-related potentials, especially when few training data points are available.