How Advanced Change Patterns Impact the Process of Process Modeling

Process model quality has been an area of considerable research efforts. In this context, correctness-by-construction as enabled by change patterns provides promising perspectives. While the process of process modeling (PPM) based on change primitives has been thoroughly investigated, only little is known about the PPM based on change patterns. In particular, it is unclear what set of change patterns should be provided and how the available change pattern set impacts the PPM. To obtain a better understanding of the latter as well as the (subjective) perceptions of process modelers, the arising challenges, and the pros and cons of different change pattern sets we conduct a controlled experiment. Our results indicate that process modelers face similar challenges irrespective of the used change pattern set (core pattern set versus extended pattern set, which adds two advanced change patterns to the core patterns set). An extended change pattern set, however, is perceived as more difficult to use, yielding a higher mental effort. Moreover, our results indicate that more advanced patterns were only used to a limited extent and frequently applied incorrectly, thus, lowering the potential benefits of an extended pattern set

Towards an Intelligent Workflow Designer based on the Reuse of Workflow Patterns

In order to perform process-aware information systems we need sophisticated methods and concepts for designing and modeling processes. Recently, research on workflow patterns has emerged in order to increase the reuse of recurring workflow structures. However, current workflow modeling tools do not provide functionalities that enable users to define, query, and reuse workflow patterns properly. In this paper we gather a suite for both process modeling and normalization based on workflow patterns reuse. This suite must be used in the extension of some workflow design tool. The suite comprises components for the design of processes from both legacy systems and process modeling

Learning the dynamics of articulated tracked vehicles

In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV

A Semantic-based framework for discovering business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modeling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. This paper focuses on business process patterns and proposes an initial framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework synthesizes the idea from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Deterministic Petri net languages as business process specification language.

Today, a wide variety of techniques have been proposed to model the process aspects of business processes. The problem, however, is that many of these are focused on providing a clear graphical representation of the models and give almost no support for complex verification procedures. Alternatively, the use of Petri Nets as a business process modeling language has been repeatedly proposed. In complex business processes the use of Petri Nets has been criticized and the technique is believed to be unable to capture such processes in all aspects. Therefore, in this paper, we introduce the application of Petri Net language theory for business process specification. Petri Net languages are an extension to the Petri Net theory, and they provide a set of techniques to describe complex business processes more efficiently. More specifically, we advocate the application of deterministic Petri Net languages to model the control flow aspects of business processes. The balance between modeling power and analysis possibilities makes deterministic Petri Nets a highly efficient technique, used in a wide range of domains. The proof of their usability, as business process specification language, is given by providing suitable solutions to model the basic and more complex business process patterns [4]. Additionally, some points of particular interest are concisely discussed.Business; Business process modeling; Control; Model; Models; Patterns; Petri Net theory; Power; Process modeling; Processes; Representation; Theory; Verification;

Visualization of Business Process Modeling Anti Patterns

Patterns are used to capture and document frequent design activities. Patterns are means to compare the expressiveness of different modeling languages. On the other hand, the term $anti-pattern$ points to undesirable design activities. In the field of business process modeling, useful patterns were collected to help evaluate models and tools. Nevertheless, there was almost no work to capture the unwanted design patterns. The most common way to model business processes is to use a graphical modeling language. The most widespread notation are business process diagrams modeled in the language BPMN. In this paper, we formalize structural patterns that can lead to control flow errors in such graphical models. For expressing such error patterns, we use the visual query language BPMN-Q . By using a query processor, a business process modeler is able to identify possible errors in business process diagrams. Moreover, the erroneous parts of the business process diagram can be highlighted when an instance of an error pattern is found. This way, the modeler gets an easy-to-understand feedback in the visual modeling language he or she is familiar with

Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations

Microsimulation is becoming increasingly important in traffic demand modeling. The major advantage over traditional four-step models is the ability to simulate each traveler individually. Decision-making processes can be included for each individual. Traffic demand is the result of the different decisions made by individuals; these decisions lead to plans that the individuals then try to optimize. Therefore, such microsimulation models need appropriate initial demand patterns for all given individuals. The challenge is to create individual demand patterns out of general input data. In practice, there is a large variety of input data, which can differ in quality, spatial resolution, purpose, and other characteristics. The challenge for a flexible demand-modeling framework is to combine the various data types to produce individual demand patterns. In addition, the modeling framework has to define precise interfaces to provide portability to other models, programs, and frameworks, and it should be suitable for large-scale applications that use many millions of individuals. Because the model has to be adaptable to the given input data, the framework needs to be easily extensible with new algorithms and models. The presented demand-modeling framework for large-scale scenarios fulfils all these requirements. By modeling the demand for two different scenarios (Zurich, Switzerland, and the German states of Berlin and Brandenburg), the framework shows its flexibility in aspects of diverse input data, interfaces to third-party products, spatial resolution, and last but not least, the modeling process itself