Albert Maier

BPELDT — Data-Aware Extension for Data-Intensive Service Applications

Aside from business processes, the service-oriented approach —currently realized with Web services and BPEL—should be utilizable for data-intensive applications as well. Fundamentally, data-intensive applications are characterized by (i) a sequence of functional operations processing large amounts of data and (ii) the delivery and transformation of huge data sets between those functional activities. However, for the efficient handling of massive data sets, a significant amount of data infrastructure is required and the predefined ‘by value’ data semantic within the invocation of Web services and BPEL is not well suited for this context. To tackle this problem on the BPEL level, we developed a seamless extension to BPEL—the ‘BPEL data transitions’.

Data-aware SOA for Gene Expression Analysis Processes

In the context of genome research, the method of gene expression analysis has been used for several years. Related microarray experiments are conducted all over the world, and consequently, a vast amount of microarray data sets are produced. Having access to this variety of repositories, researchers would like to incorporate this data in their analyses processes to increase the statistical significance of their results. Such analyses processes are typical examples of data-intensive processes. In general, data-intensive processes are characterized by (i) a sequence of functional operations processing large amount of data and (ii) the transportation and transformation of huge data sets between the functional operations. To support data-intensive processes, an efficient and scalable environment is required, since the performance is a key factor today. The service-oriented architecture (SOA) is beneficial in this area according to process orchestration and execution. However, the current realization of SOA with Web services and BPEL includes some drawbacks with regard to the performance of the data propagation between Web services. Therefore, we present in this paper our data-aware service-oriented approach to efficiently support such data-intensive processes.

Industrializing Data Integration Projects using a Metadata Driven Assembly Line

Abstract Data integration is essential for the success of many enterprise business initiatives, but also a very significant contributor to the costs and risks of the IT projects supporting these initiatives. Highly skilled consultants and data stewards re-design the usage of data in business processes, define the target landscape and its data models, and map the current information landscape into the target landscape. Still, the largest part of a typical data integration effort is dedicated to the implementation of transformation, cleansing, and data validation logic in robust and highly performing commercial systems. This effort is simple and doesn´t demand skills beyond commercial product knowledge, but it is very labour-intensive and error prone. In this paper we describe a new commercial approach to data integration that helps to “industrialize” data integration projects and significantly lowers the amount of simple, but labour-intensive work. The key idea is that the target landscape for a data integration project has pre-defined data models and associated meta data which can be leveraged for building and automating the data integration process. This approach has been implemented in the context of the support of SAP consolidation projects and is used in some of the largest data integration projects world-wide. Zusammenfassung Bei vielen Umstrukturierungsprojekten in Unternehmen spielt die Datenintegration eine entscheidende Rolle. In den zugehörigen IT Projekten sind ein signifikanter Teil der Kosten sowie des Projektrisikos auf Datenintegration zurückzuführen. Hochdotierte Berater und Datenverantwortliche gestalten die Verwendung der Daten in Geschäftsprozessen neu, definieren die zukünftige IT Landschaft und deren Datenmodelle, und erstellen Abbildungsvorschriften zwischen alten und neuen Anwendungssystemen. Trotzdem steckt ein Großteil des Aufwands von Datenintegrationsprojekten immer noch in der Implementierung von Transformationsvorschriften, Datenaufbereitungs- und Validierungslogik in hochperformanten kommerziellen Systemen. Diese Tätigkeiten sind relativ einfach und verlangen nur Kenntnisse in der eingesetzten Basissoftware. Jedoch sind diese Tätigkeiten arbeitsintensiv und fehleranfällig. In diesem Artikel beschreiben wir einen neuen kommerziellen Ansatz für Datenintegrationsprojekte, welcher diese “industrialisiert” und dabei die einfachen, aber fehleranfälligen Arbeitsschritte signifikant reduziert. Der Ansatz basiert auf der Ausnutzung von Datenmodellen und Metadaten der neuen Anwendungssysteme zur Automatisierung der Datenintegrationsprozesse und zur Generierung der den Prozessschritten zu Grunde liegenden Artefakte. Dieser Ansatz wurde zur Unterstützung von SAP Konsolidierungsprojekten entwickelt und wird derzeit in einigen der weltweit größten Datenintegrationsprojekten eingesetzt.