Integrating Mobile Tasks with Business Processes: A Self-Healing Approach

Process management technology constitutes a fundamental component of any service-driven computing environment. Process management facilitates both the composition of services at design time and their orchestration at run time. In particular, when applying the service paradigm to enterprise integration management, high flexibility is required. In this context, atomic as well as composite services representing the business functions should be quickly adaptable to cope with dynamic business changes. Furthermore, they should enable mobile and quick access to enterprise information. The growing maturity of smart mobile devices has fostered their prevalence in knowledge-intensive areas in the enterprise as well. As a consequence, process management technology needs to be enhanced with mobile task support. However, tasks hitherto executed stationarily, cannot be simply transferred in order to run on smart mobile devices. Many research groups focus on the partitioning of processes and the distributed execution of the resulting fragments on smart mobile devices. Opposed to this fragmentation concept, this chapter proposes an approach to enable the robust and flexible execution of single process tasks on smart mobile devices by provisioning self-healing techniques to address the smooth integration of mobile tasks with business processes

Collaboration Support Through Mobile Processes and Entailment Constraints

The computational capability of smart mobile devices increasingly fosters their prevalence in many business domains. Along this trend, process management technology is going to be enhanced with mobile task support. However, tasks executed stationarily so far cannot be simply transfered to mobile devices. For the latter purpose, we developed an approach within the MARPLE project enabling mobile and robust task execution in the context of business processes. In particular, this approach provides self-healing techniques that relieve mobile users from manually handling errors (e.g., lost connections) during mobile task execution. In this paper, we extend the collaboration facilities of our approach by adding entailment constraints to mobile task management. In the context of a business process, for example, two tasks may have to be executed by the same (mobile) user. Related research on integrating such constraints with business processes has received growing attention recently. However, realizing entailment constraints in the context of mobile processes and tasks raises additional issues, which must be probably integrated with the mentioned error handling techniques. We present fundamental entailment constraints supported by our approach and discuss how they can be realized in a robust and flexible manner. In particular, this will significantly enhance mobile task and process support in next generation information systems

Untersuchung von ortsgebundenen Diensten am Beispiel des Android Frameworks

Diese Arbeit betrachtet und evaluiert ortsbezogene Dienste im Rahmen der Entwicklung einer App für das Android 2.2 System. In Zusammenarbeit mit der Hubermedia GmbH soll eine Anwendung entwickelt werden, welche das Kartenmaterial und die ortsbezogenen Informationen, z. B. zu Gaststätten oder Wanderrouten, welche von Hubermedia gestellt werden, anzeigt. Die App soll den bereits entwickelten IPhone Apps ähneln und deren Funktionen komplett widerspiegeln. Dazu gehört die Anzeige von verschiedenem Kartenmaterial, die Anzeige von ortsbezogenen Informationen auf der Karte und die Anzeige detaillierter Informationen. Sowohl die Informations- als auch die Kartendaten werden von einem Web Service der Hubermedia GmbH gestellt und erforderten die Implementierung einer Schnittstelle

A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses

Seibold H, Czerny S, Decke S, et al. A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses. PLoS ONE . 2021;16(6): e0251194.Computational reproducibility is a corner stone for sound and credible research. Especially in complex statistical analyses-such as the analysis of longitudinal data-reproducing results is far from simple, especially if no source code is available. In this work we aimed to reproduce analyses of longitudinal data of 11 articles published in PLOS ONE. Inclusion criteria were the availability of data and author consent. We investigated the types of methods and software used and whether we were able to reproduce the data analysis using open source software. Most articles provided overview tables and simple visualisations. Generalised Estimating Equations (GEEs) were the most popular statistical models among the selected articles. Only one article used open source software and only one published part of the analysis code. Replication was difficult in most cases and required reverse engineering of results or contacting the authors. For three articles we were not able to reproduce the results, for another two only parts of them. For all but two articles we had to contact the authors to be able to reproduce the results. Our main learning is that reproducing papers is difficult if no code is supplied and leads to a high burden for those conducting the reproductions. Open data policies in journals are good, but to truly boost reproducibility we suggest adding open code policies