Workflow technology has become a standard solution for managing increasingly complex business processes. Successful business process management depends on effective workflow modeling, which has been limited mainly to modeling the control and coordination of activities, i.e. the control flow perspective. However, given a workflow specification that is flawless from the control flow perspective, errors can still occur due to incorrect dataflow specification, which is referred to as dataflow anomalies.Currently, there are no sufficient formalisms for discovering and preventing dataflow anomalies in a workflow specification. Therefore, the goal of this dissertation is to develop formal methods for automatically detecting dataflow anomalies from a given workflow model and a rigorous approach for workflow design, which can help avoid dataflow anomalies during the design stage.In this dissertation, we first propose a formal approach for dataflow verification, which can detect dataflow anomalies such as missing data, redundant data, and potential data conflicts. In addition, we propose to use the dataflow matrix, a two-dimension table showing the operations each activity has on each data item, as a way to specify dataflow in workflows. We believe that our dataflow verification framework has added more analytical rigor to business process management by enabling systematic elimination of dataflow errors.We then propose a formal dependency-analysis-based approach for workflow design. A new concept called "activity relations" and a matrix-based analytical procedure are developed to enable the derivation of workflow models in a precise and rigorous manner. Moreover, we decouple the correctness issue from the efficiency issue as a way to reduce the complexity of workflow design and apply the concept of inline blocks to further simplify the procedure. These novel techniques make it easier to handle complex and unstructured workflow models, including overlapping patterns.In addition to proving the core theorems underlying the formal approaches and illustrating the validity of our approaches by applying them to real world cases, we provide detailed algorithms and system architectures as a roadmap for the implementation of dataflow verification and workflow design procedures.

The Ph.D. project has evolved from focusing on the technical problem of the integration and interoperability of an assemblage of complex systems and SoS within a flight training system to development of a workflow process using frameworks to aid the decision making process for the selection of optimal flight training blending mixes. The focus of the research involved developing a methodology to satisfy research project proposal requirements agreed upon with the industrial sponsor. This thesis investigates the complexity of a modern flight training systems and the need for understanding that it is supported by a complex Family of Systems (FoS) including Virtual Reality Training Environments such as flight simulators, to live training aircraft with various configurations of avionic controls. One of the key technical problems today is how best to develop and assemble a family of flight training system into an integrated Live/Synthetic mix for aircrew training to optimise organisation and training objectives. With the increased use of emulation/synthetic data on aircraft for live training, the synthetic boundary is becoming increasingly blurred. Systematic consideration of the most appropriate blend is needed. The methodology used in the research is model driven and the architecture produced is described at a level of abstraction to enable communication to all stakeholders for the means of understanding the structure involved in the system design process. Relational Oriented Systems Engineering and Technology Trade-Off Analysis (ROSETTA) frameworks are described using Model Based Systems Engineering (MBSE) techniques for supporting capability based trade-off decisions for selection of optimal flight training FoS mixes dependent on capability. The research proposes a methodology and associated methods including a high-level systematic closed loop information management structure for blended device/tool aircrew training and a modelling and analysis approach for the FoS aviation training problem to enhance the existing training programmes to provide a more efficient and agile training environment. The mathematical formalisms used provide a method of quantifying subjective opinions and judgements for trade studies to be accomplished on the suitability of technology for each student pilot in relation to training and organisational objectives. The methodology presented is by no means a final solution, but a path for further research to enable a greater understanding of the suitability of training tools/technology used to train individual pilots at various stages throughout the training pipeline lifecycle(s).