Saying Hello World with MOLA - A Solution to the TTC 2011 Instructive Case

This paper describes the solution of Hello World transformations in MOLA transformation language. Transformations implementing the task are relatively straightforward and easily inferable from the task specification. The required additional steps related to model import and export are also described.Comment: In Proceedings TTC 2011, arXiv:1111.440

Solving the TTC 2011 Reengineering Case with MOLA and Higher-Order Transformations

The Reengineering Case of the Transformation Tool Contest 2011 deals with automatic extraction of state machine from Java source code. The transformation task involves complex, non-local matching of model elements. This paper contains the solution of the task using model transformation language MOLA. The MOLA solution uses higher-order transformations (HOT-s) to generate a part of the required MOLA program. The described HOT approach allows creating reusable, complex model transformation libraries for generic tasks without modifying an implementation of a model transformation language. Thus model transformation users who are not the developers of the language can achieve the desired functionality more easily.Comment: In Proceedings TTC 2011, arXiv:1111.440

Towards a more effective hospital: helping health professionals to learn from their own practice by developing an easy to use clinical processes querying language

Application of complex socio-technical systems theory to optimization of clinical processes in hospitals highlights the importance of the acceptance and promotion of responsible autonomy among health professionals. Therefore the independent ability for clinicians to search for answers to questions which are outside the scope of pre-made reports is important. However, the ad-hoc data querying process is slow and error prone due to inability of health professionals to access data directly without involving IT experts. The problem lies in the complexity of means used to query data. We propose a new natural language- and star ontology-based ad-hoc data querying approach which reduces the steep learning curve required to be able to query data. The proposed approach would significantly decrease the time needed to master the ad-hoc data querying and to obtain direct access to data by health professionals