Higher-Order Process Modeling: Product-Lining, Variability Modeling and Beyond

We present a graphical and dynamic framework for binding and execution of business) process models. It is tailored to integrate 1) ad hoc processes modeled graphically, 2) third party services discovered in the (Inter)net, and 3) (dynamically) synthesized process chains that solve situation-specific tasks, with the synthesis taking place not only at design time, but also at runtime. Key to our approach is the introduction of type-safe stacked second-order execution contexts that allow for higher-order process modeling. Tamed by our underlying strict service-oriented notion of abstraction, this approach is tailored also to be used by application experts with little technical knowledge: users can select, modify, construct and then pass (component) processes during process execution as if they were data. We illustrate the impact and essence of our framework along a concrete, realistic (business) process modeling scenario: the development of Springer's browser-based Online Conference Service (OCS). The most advanced feature of our new framework allows one to combine online synthesis with the integration of the synthesized process into the running application. This ability leads to a particularly flexible way of implementing self-adaption, and to a particularly concise and powerful way of achieving variability not only at design time, but also at runtime.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455

Automating the referral pathways for Multiple Myeloma through a Web Application and XMDD

Multiple Myeloma (MM), a type of bone marrow cancer, is diagnosed by measuring monoclonal proteins, paraproteins (PP), and serum-free light chains (SFLC) in the blood. These proteins can be detected in healthy individuals at a lower level. This condition is called Monoclonal Gammopathy of Uncertain Significance (MGUS). MGUS is associated with a risk of progression to MM at a rate of 1-2% per year. Early diagnosis of MM correlates with improved overall survival for patients, so early referral of suspect cases is important. Two risk factors determine the risk of progression: a high-level PP (>15g/l) and an abnormal SFLC ratio. This risk stratification process enables General Practitioners (essentially, the family doctors) to manage the patients with low-risk MGUS and provides clear referral pathways for intermediate and high-risk MGUS patients. There are a reference algorithm and a scoring system for patient referrals with possible Multiple Myeloma, that in the current practice are processed manually by trained healthcare staff. In collaboration with the Haematology experts at the University Hospital Limerick and the SCCE group in Computer Science, we designed and implemented a software application that improves and streamlines the current process. This (online) application is developed with modern XMDD technology, using the DIME low-code application development tool. The application faithfully maps the reference algorithm in an automated way and applies it to a consultation data-set. The novelty consists in the adopted technologies, that improve the early validation and correctness of the software, and ease the human understanding and the modification turnaround of the application

DSL-based Interoperability and Integration in the Smart Manufacturing Digital Thread

In the industry 4.0 ecosystem, a Digital Thread connects the data and processes for smarter manufacturing. It provides an end to end integration of the various digital entities thus fostering interoperability, with the aim to design and deliver complex and heterogeneous interconnected systems. We develop a service oriented domain specific Digital Thread platform in a Smart Manufacturing research and prototyping context. We address the principles, architecture and individual aspects of a growing Digital Thread platform. It conforms to the best practices of coordination languages, integration and interoperability of external services from various platforms, and provides orchestration in a formal methods based, low-code and graphical model driven fashion. We chose the Cinco products DIME and Pyrus as the underlying IT platforms for our Digital Thread solution to serve the needs of the applications addressed: manufacturing analytics and predictive maintenance are in fact core capabilities for the success of smart manufacturing operations. In this regard, we extend the capabilities of these two platforms in the vertical domains of data persistence, IoT connectivity and analytics, to support the basic operations of smart manufacturing. External native DSLs provide the data and capability integrations through families of SIBs. The small examples constitute blueprints for the methodology, addressing the knowledge, terminology and concerns of domain stakeholders. Over time, we expect reuse to increase, reducing the new integration and development effort to a progressively smaller portion of the models and code needed for at least the most standard application