Orchestration languages are of paramount importance for building composite services in service-oriented architectures. Pattern-based analysis is a method that allows to determine the expressiveness of existing process languages and serves as a means of comparison between different languages. The aim of this study is the analysis and comparison of important languages for building Web Services-based orchestrations, as well as the improvement of the method of pattern-based analysis. The predominant orchestration language today is the Web Services Business Process Execution Language (WS-BPEL) 2.0. This language is a standard that has been implemented by several companies and projects, such as the OpenESB BPEL Service Engine. An additional language is Windows Workflow 4 that is shipped by Microsoft as part of the .NET framework. There are various aspects, represented by pattern catalogs, for which existing languages can be analyzed. This study suggests a methodology for ordering existing pattern catalogs according to their importance for a selected problem domain which is Business-to-Business Integration. It furthermore presents an extensive evaluation of the languages at hand and assesses the degree of support they provide for several of the most important pattern catalogs. These catalogs are the workflow control-flow patterns, the service interaction patterns, the change patterns and the time patterns.
By using the remote functions of a modern IT service management system infrastructure, it is possible to analyze huge amounts of logfile data from complex technical equipment. This enables a service provider to predict failures of connected equipment before they happen. The problem most providers face in this context is finding a needle in a haystack - the obtained amount of data turns out to be too large to be analyzed manually. This report describes a process to find suitable predictive patterns in log files for the detection of upcoming critical situations. The identification process may serve as a hands-on guide. It describes how to connect statistical means, data mining algorithms and expert domain knowledge in the domain of service management. The process was developed in a research project which is currently being carried out within the Siemens Healthcare service organization. The project deals with two main aspects: First, the identification of predictive patterns in existing service data and second, the architecture of an autonomous agent which is able to correlate such patterns. This paper summarizes the results of the first project challenge. The identification process was tested successfully in a proof of concept for several Siemens Healthcare products.