Michael Zeising

ESProNa: Constraint-Based Declarative Business Process Modeling

In this paper we describe how declarative process modeling together with ontologies can be used to build complex clinical process models. Our approach supports the definition of functional, behavioral, organizational, data and operational process perspectives, resulting in an expressive and flexible modeling language. We use constraints for representing inter-process dependencies and constraint propagation for finding which processes are executable in user selected or given scenarios. Knowledge about the organizational perspective of a clinical ontology can be represented and imported from RDF files for interfacing with other applications. We implemented our approach in ESProNa, a Log talk application running on SWI-Prolog extended with the CLP(FD) constraint library and the N3 parser Henry.

Supporting Rule-Based Process Mining by User-Guided Discovery of Resource-Aware Frequent Patterns

Agile processes depend on human resources, decisions and expert knowledge and are especially versatile and comprise rather complex coherencies. Rule-based process models are well-suited for modeling these processes. There exist a number of process mining approaches to discover rule-based process models from event logs. However, existing rule-based approaches are typically based on a given set of rule templates and predominately consider control flow aspects. By only considering a given set of templates, contemporary approaches underlie a representational bias. The usage of a fixed language frequently ends into insuffcient languages. In this paper we propose an approach to automatically suggest adequate resource-aware rule templates for a given domain by pre-processing the provided event log using frequent pattern mining techniques. These templates can then be instantiated and checked by process mining methods.

Towards Location-Aware Declarative Business Process Management

Business process modelling usually involves perspectives like the functional (what), the organizational (who), the data-based (consuming and producing which information) and the behavioural (when) perspective. However, the so-called “locational” perspective is either neglected or vaguely contained in one of the others. A locational perspective implies that locations are treated as “first-class” modelling entities like processes and data objects. The assignment of tasks to participants and the progression of a process may then depend on these locations. This contribution describes how such location aware processes may be modelled and how a process execution system can be extended in a way so that it interprets these processes.

Natural Language Generation for Declarative Process Models

Two different types of processes can be distinguished: well-structured routine processes and agile processes where the control-flow cannot be predefined a priori. In a similar way, two modeling paradigms exist whereby procedural models are more adequate for routine processes and declarative models are more suitable for agile processes. Often business analysts are not confident in understanding process models; this holds even more for declarative process models. Natural language support for this kind of processes in order to improve their readability is desirable. In the work at hand we define a technique that transforms declarative models to intuitive natural language texts. Hereof, the approach focuses on content determination and structuring the output texts.