International Journal of Cooperative Information Systems

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.

Reference models for data analysis with data warehouses may consist of multidimensional reference models and analysis graphs. Multidimensional reference models are best-practice domain-specific data models for online analytical processing. Analysis graphs are reference models of analysis processes for event-driven data analysis. Small and medium-sized enterprises (SMEs) as well as large multinational companies may benefit from the use of reference models for data analysis. The availability of multidimensional reference models lowers the obstacles that inhibit SMEs from using business intelligence (BI) technology. Multinational companies may define multidimensional reference models for increased compliance among subsidiaries and departments. Furthermore, the definition of analysis graphs facilitates the handling of business events for both SMEs and large companies. Modelers may customize the chosen reference models, tailoring the models to the specific needs of the individual company or local subsidiary. Customizations may consist of additions, omissions, and modifications with respect to the reference model. In this paper, we propose a metamodel and customization approach for multidimensional reference models and analysis graphs. We specifically address the explicit modeling of key performance indicators as well as the definition of analysis situations and analysis graphs.

Combining enterprise architecture and operational data is complex (especially when considering the actual ‘matching’ of data with enterprise architecture elements), and little has been written on how to do this. In this paper we aim to fill this gap, and propose a method to combine operational data with enterprise architecture to better support decision-making. Using such a method may result in either an enriched enterprise architecture model (which is very suitable as basis for model-based architecture analyses) or a warehouse data model where operational data is enriched with enterprise architecture metadata (which leads to more traceability by easing the retrieval and interpretation of raw data and of business analytics results). The method is illustrated by means of a case and evaluated by experts. Also, a model for mapping enterprise architecture, operational data, and time is proposed, which allows the model-based execution of new types of analyses.