Process models support the transition from business requirements to IT implementations. An organization that adopts process modeling often maintain several co-existing models of the same business process. These models target different abstraction levels and stakeholder perspectives. Maintaining consistency among these models has become a major challenge for such an organization. For instance, propagating changes requires identifying tacit correspondences among the models, which may be only in the memories of their original creators or may be lost entirely. Although different tools target specific needs of different roles, we lack appropriate support for checking whether related models maintained by different groups of specialists are still consistent after independent editing. As a result, typical consistency management tasks such as tracing, differencing, comparing, refactoring, merging, conformance checking, change notification, and versioning are frequently done manually, which is time-consuming and error-prone. This thesis presents the Shared Model, a framework designed to improve support for consistency management and impact analysis in process modeling. The framework is designed as a result of a comprehensive industrial study that elicited typical correspondence patterns between Business and IT process models and the meaning of consistency between them. The framework encompasses three major techniques and contributions: 1) matching heuristics to automatically discover complex correspondences patterns among the models, and to maintain traceability among model parts---elements and fragments; 2) a generator of edit operations to compute the difference between process models; 3) a process model synchronizer, capable of consistently propagating changes made to any model to its counterpart. We evaluated the Shared Model experimentally. The evaluation shows that the framework can consistently synchronize Business and IT views related by correspondence patterns, after non-simultaneous independent editing.

There is a wide variety of drivers for business process modelling initiatives, reaching from organisational redesign to the development of information systems. Consequently, a common business process is often captured in multiple models that overlap in content due to serving different purposes. Business process management aims at exible adaptation to changing business needs. Hence, changes of business processes occur frequently and have to be incorporated in the respective process models. Once a process model is changed, related process models have to be updated accordingly, despite the fact that those process models may only be loosely coupled. In this article, we introduce an approach that supports change propagation between related process models. Given a change in one process model, we leverage the behavioural abstraction of behavioural profiles for corresponding activities in order to determine a change region in another model. Our approach is able to cope with changes in pairs of models that are not related by hierarchical refinement and show behavioural inconsistencies. We evaluate the applicability of our approach with two real-world process model collections. To this end, we either deduce change operations from different model revisions or rely on synthetic change operations.

We introduce a hybrid approach in which images of an urban scene are automatically alignedwith a base geometry of the scene to determine model-relative external camera parameters. Thealgorithm takes as input a model of the scene and images with approximate external cameraparameters and aligns the images to the model by extracting the facades from the images andaligning the facades with the model by minimizing over a multivariate objective function. Theresulting image-pose pairs can be used to render photo-realistic views of the model via texturemapping.Several natural extensions to the base hybrid reconstruction technique are also introduced. Theseextensions, which include vanishing point based calibration refinement and video stream basedreconstruction, increase the accuracy of the base algorithm, reduce the amount of data that mustbe provided by the user as input to the algorithm, and provide a mechanism for automaticallycalibrating a large set of images for post processing steps such as automatic model enhancementand fly-through model visualization.Traditionally, photo-realistic urban reconstruction has been approached from purely image-basedor model-based approaches. Recently, research has been conducted on hybrid approaches, whichcombine the use of images and models. Such approaches typically require user assistance forcamera calibration. Our approach is an improvement over these methods because it does notrequire user assistance for camera calibration.