Ubiquitous computing is considered as enabler for linking everyday life with information and communication technology. However, developing pervasive and mobile applications that provide personalized user assistance still constitutes a challenge. Mobile application scenarios are diverse and encompass domains like healthcare, logistics, and sales. For their support two fundamental technologies with increasing maturity are emerging: development frameworks for mobile devices and lightweight process engines. Their integrated use, however, is in a rather premature state. Generally, the use of a process engine for supporting mobile collaboration raises many challenging issues. This paper picks up some of these challenges and shows how we have coped with them in the MARPLE project. MARPLE targets at a tight integration of process management technology with mobile computing frameworks in order to enable mobile process support in advanced application scenarios. We give insights into the MARPLE architecture and its components. In particular, we introduce the MARPLE process engine, which enables lightweight as well as flexible process support on mobile devices. This will be key for mobile user assistance in advanced application scenarios.

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In a service-oriented architecture (SOA), a change or shutdown of a particular service might have a significant impact on its consumers (e.g., IT systems). To effectively cope with such situations, the IT systems affected by a service change should be identified before actually applying the latter. For this purpose, a SOA repository with advanced analysis capabilities is needed. However, due to the numerous complex inter-dependencies between service providers and consumers, it is a challenging task to figure out which IT systems might be directly or indirectly affected by a service change and for which period of time this applies. The paper tackles this challenge and presents the design of an advanced SOA repository enriched with analysis capabilities. In particular, this repository enables automatic analyses to detect already existing problems (as-is analyses) as well as problems that might occur due to future service changes (whatif analyses). Respective analyses will foster the development of robust service-oriented applications.

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