Enhancing the Guidance of the Intentional Model "MAP": Graph Theory Application

The MAP model was introduced in information system engineering in order to model processes on a flexible way. The intentional level of this model helps an engineer to execute a process with a strong relationship to the situation of the project at hand. In the literature, attempts for having a practical use of maps are not numerous. Our aim is to enhance the guidance mechanisms of the process execution by reusing graph algorithms. After clarifying the existing relationship between graphs and maps, we improve the MAP model by adding qualitative criteria. We then offer a way to express maps with graphs and propose to use Graph theory algorithms to offer an automatic guidance of the map. We illustrate our proposal by an example and discuss its limitations.Comment: 9 page

Investigating expressiveness and understandability of hierarchy in declarative business process models

Hierarchy has widely been recognized as a viable approach to deal with the complexity of conceptual models. For instance, in declarative business process models, hierarchy is realized by sub-processes. While technical implementations of declarative sub-processes exist, their application, semantics, and the resulting impact on understandability are less understood yet—this research gap is addressed in this work. More specifically, we discuss the semantics and the application of hierarchy and show how subprocesses enhance the expressiveness of declarative modeling languages. Then, we turn to the influence of hierarchy on the understandability of declarative process models. In particular, we present a cognitive-psychology-based framework that allows to assess the impact of hierarchy on the understandability of a declarative process model. To empirically test the proposed framework, a combination of quantitative and qualitative research methods is followed. While statistical tests provide numerical evidence, think-aloud protocols give insights into the reasoning processes taking place when reading declarative process models

Goal-Driven Multi-Process Analysis.

Extant process modeling techniques address different aspects of processes, such as activity sequencing, resource allocation, and organizational responsibilities. These techniques are usually based on graphic notation and are driven by practice rather than by theoretical foundations. The lack of theoretical principles hinders the ability to ascertain the correctness of a process model. A few techniques (notably Petri Nets) are formalized and apply verification mechanisms (mostly for activity sequencing and concurrency). However, these techniques do not deal with important aspects of process design such as process goals. As previously suggested, a formal process modeling framework, termed the Generic Process Model (GPM), has been used to define the notion of process model validity. In GPM, validity is based on the idea that the purpose of process design is to assure that an enacted process can reach its goal. In practice, often several processes work together to accomplish goals in an organizational domain. Accordingly, in this paper we extend the validity analysis of a single process to a cluster of processes related by the exchange of physical entities or information. We develop validity criteria and demonstrate their application to models taken from the Supply Chain Operations Reference-model (SCOR). We also use the formal concepts to analyze the role of an information system in inter-process communication and its possible effects on process cluster validity

Unsupervised discovery of intentional process models from event logs

International audienceResearch on guidance and method engineering has highlighted that many method engineering issues, such as lack of flexibility or adaptation, are solved more effectively when intentions are explicitly specified. However, software engineering process models are most often described in terms of sequences of activities. This paper presents a novel approach, so-called Map Miner Method (MMM), designed to automate the construction of intentional process models from process logs. To do so, MMM uses Hidden Markov Models to model users' activities logs in terms of users' strategies. MMM also infers users' intentions and constructs fine-grained and coarse-grained intentional process models with respect to the Map metamodel syntax (i.e., metamodel that specifies intentions and strategies of process actors). These models are obtained by optimizing a new precision-fitness metric. The result is a software engineering method process specification aligned with state of the art of method engineering approaches. As a case study, the MMM is used to mine the intentional process associated to the Eclipse platform usage. Observations show that the obtained intentional process model offers a new understanding of software processes, and could readily be used for recommender systems