@article{KunzeWeidlichWeske2015, author = {Kunze, Matthias and Weidlich, Matthias and Weske, Mathias}, title = {Querying process models by behavior inclusion}, series = {Software and systems modeling}, volume = {14}, journal = {Software and systems modeling}, number = {3}, publisher = {Springer}, address = {Heidelberg}, issn = {1619-1366}, doi = {10.1007/s10270-013-0389-6}, pages = {1105 -- 1125}, year = {2015}, abstract = {Business processes are vital to managing organizations as they sustain a company's competitiveness. Consequently, these organizations maintain collections of hundreds or thousands of process models for streamlining working procedures and facilitating process implementation. Yet, the management of large process model collections requires effective searching capabilities. Recent research focused on similarity search of process models, but querying process models is still a largely open topic. This article presents an approach to querying process models that takes a process example as input and discovers all models that allow replaying the behavior of the query. To this end, we provide a notion of behavioral inclusion that is based on trace semantics and abstraction. Additional to deciding a match, a closeness score is provided that describes how well the behavior of the query is represented in the model and can be used for ranking. The article introduces the formal foundations of the approach and shows how they are applied to querying large process model collections. An experimental evaluation has been conducted that confirms the suitability of the solution as well as its applicability and scalability in practice.}, language = {en} } @article{RoggeSoltiWeske2015, author = {Rogge-Solti, Andreas and Weske, Mathias}, title = {Prediction of business process durations using non-Markovian stochastic Petri nets}, series = {Information systems}, volume = {54}, journal = {Information systems}, publisher = {Elsevier}, address = {Oxford}, issn = {0306-4379}, doi = {10.1016/j.is.2015.04.004}, pages = {1 -- 14}, year = {2015}, abstract = {Companies need to efficiently manage their business processes to deliver products and services in time. Therefore, they monitor the progress of individual cases to be able to timely detect undesired deviations and to react accordingly. For example, companies can decide to speed up process execution by raising alerts or by using additional resources, which increases the chance that a certain deadline or service level agreement can be met. Central to such process control is accurate prediction of the remaining time of a case and the estimation of the risk of missing a deadline. To achieve this goal, we use a specific kind of stochastic Petri nets that can capture arbitrary duration distributions. Thereby, we are able to achieve higher prediction accuracy than related approaches. Further, we evaluate the approach in comparison to state of the art approaches and show the potential of exploiting a so far untapped source of information: the elapsed time since the last observed event. Real-world case studies in the financial and logistics domain serve to illustrate and evaluate the approach presented. (C) 2015 Elsevier Ltd. All rights reserved.}, language = {en} } @article{HerzbergMeyerWeske2015, author = {Herzberg, Nico and Meyer, Andreas and Weske, Mathias}, title = {Improving business process intelligence by observing object state transitions}, series = {Data \& knowledge engineering}, volume = {98}, journal = {Data \& knowledge engineering}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0169-023X}, doi = {10.1016/j.datak.2015.07.008}, pages = {144 -- 164}, year = {2015}, abstract = {During the execution of business processes several events happen that are recorded in the company's information systems. These events deliver insights into process executions so that process monitoring and analysis can be performed resulting, for instance, in prediction of upcoming process steps or the analysis of the run time of single steps. While event capturing is trivial when a process engine with integrated logging capabilities is used, manual process execution environments do not provide automatic logging of events, so that typically external devices, like bar code scanners, have to be used. As experience shows, these manual steps are error-prone and induce additional work. Therefore, we use object state transitions as additional monitoring information, so-called object state transition events. Based on these object state transition events, we reason about the enablement and termination of activities and provide the basis for process monitoring and analysis in terms of a large event log. In this paper, we present the concept to utilize information from these object state transition events for capturing process progress. Furthermore, we discuss a methodology to create the required design time artifacts that then are used for monitoring at run time. In a proof-of-concept implementation, we show how the design time and run time side work and prove applicability of the introduced concept of object state transition events. (C) 2015 Elsevier B.V. All rights reserved.}, language = {en} } @article{MeyerPufahlBatoulisetal.2015, author = {Meyer, Andreas and Pufahl, Luise and Batoulis, Kimon and Fahland, Dirk and Weske, Mathias}, title = {Automating data exchange in process choreographies}, series = {Information systems}, volume = {53}, journal = {Information systems}, publisher = {Elsevier}, address = {Oxford}, issn = {0306-4379}, doi = {10.1016/j.is.2015.03.008}, pages = {296 -- 329}, year = {2015}, abstract = {Communication between organizations is formalized as process choreographies in daily business. While the correct ordering of exchanged messages can be modeled and enacted with current choreography techniques, no approach exists to describe and automate the exchange of data between processes in a choreography using messages. This paper describes an entirely model-driven approach for BPMN introducing a few concepts that suffice to model data retrieval, data transformation, message exchange, and correlation four aspects of data exchange. For automation, this work utilizes a recent concept to enact data dependencies in internal processes. We present a modeling guideline to derive local process models from a given choreography; their operational semantics allows to correctly enact the entire choreography from the derived models only including the exchange of data. Targeting on successful interactions, we discuss means to ensure correct process choreography modeling. Finally, we implemented our approach by extending the camunda BPM platform with our approach and show its feasibility by realizing all service interaction patterns using only model-based concepts. (C) 2015 Elsevier Ltd. All rights reserved.}, language = {en} }