Business process models play an important role in the design and analysis of business operations. For this reason, many companies develop and maintain repositories in which models of business processes are centrally stored. In this talk, I will present analysis techniques for automatically extracting knowledge from such process repositories. The focus will be on leveraging these models for automatically identifying service candidates. Results are presented of applying these techniques on various process repositories from practice.
Autores: Jan Mendling /
Although several approaches for service identification have been defined in research and practice, none of them considers the potential of fully automating the associated phases. As a result, users have to invest a substantial amount of manual work. In this paper, we address the problem of manual work in the context of service identification and present an approach for automatically deriving service candidates from business process models. Our approach combines different analysis techniques in a novel way in order to derive ranked lists of service candidates. The approach is meant to be a useful aid for enabling business and IT managers to quickly spot reuse potential in their company. We demonstrate the usefulness of our approach by reporting on the results from an evaluation with a process model collection from industry.
Autores: Henrik Leopold / Jan Mendling /
Resource Assignment Language (RAL) is a language for the selection of organisational resources that can be used, for example, for the assignment of human resources to business process activities. Its formal semantics have allowed the automation of analysis operations in several phases of the business process lifecycle. RAL was designed considering a specific organisational metamodel and pursuing specific purposes. However, it can be extended to deal with similar problems in different domains and under different circumstances. In this paper, a methodology to extend RAL is introduced, and an extension to support another organisational metamodel is described as a proof-of-concept.
Autores: Cristina Cabanillas / Manuel Resinas / Antonio Ruiz-Cortés / Jan Mendling /
Palabras Clave: Business Process Management - description logics - RAL - resource assignment - W3C Organisation Ontology
Business Process Management Systems (BPMS) are increasingly used to support service composition, typically working with executable BP models that involve resources, which include both automatic services and services provided by human resources. The appropriate selection of human resources is critical, as factors such as workload or skills have an impact on work performance. While priorities for automatic services are intensively researched, human resource prioritization has been hardly discussed. In classical workflow management, only resource assignment at BP design time to select potential performers for activities, and resource allocation at run time to choose actual performers, are considered. There is no explicit consideration of prioritizing potential performers to facilitate the selection of actual performers. It is also disregarded in professional solutions.
In this paper, we address this research gap and provide two contributions: (i) we conceptually define prioritized allocation based on preferences; and (ii) we propose a concrete way in which preferences over resources can be defined so that a resource priority ranking can be automatically generated. Our solution builds on the adaptation of a user preference model developed for the discovery and ranking of semantic web services called SOUP  to the domain at hand. As a proof of concept, we have extended the resource management tool CRISTAL (http://www.isa.us.es/cristal) with the SOUP component , using RAL  for resource selection. 1. J. M. García, D. Ruiz, and A. R. Cortés, «A Model of User Preferences for Semantic
Services Discovery and Ranking,» in ESWC (2), pp. 114, Springer, 2010. 2. J. M. García, M. Junghans, D. Ruiz, S. Agarwal, and A. R. Cortés, «Integrating
semantic Web services ranking mechanisms using a common preference model,» Knowl.-Based Syst., vol. 49, pp. 2236, 2013. 3. C. Cabanillas, M. Resinas, and A. Ruiz-Cortés, «Defining and Analysing Resource Assignments in Business Processes with RAL,» in ICSOC, vol. 7084, pp. 477486, Springer, 2011.
This work was published in ICSOC 2013, vol. 8274, 374-388. It was partially supported by the EU-FP7, the EU Commission, the Spanish and the Andalusian R&D&I programmes (grants 318275, 284860, TIN2009-07366, TIN2012-32273, TIC-5906).
Autores: Cristina Cabanillas / José María García / Manuel Resinas / David Ruiz / Antonio Ruiz-Cortés / Jan Mendling /
Resumen de artículo publicado como:
Cristina Cabanillas, David Knuplesch, Manuel Resinas, Manfred Reichert, Jan Mendling, Antonio Ruiz-Cortés: RALph: A Graphical Notation for Resource Assignments in Business Processes. International Conference on Advanced Information Systems Engineering (CAiSE) 2015: 53-68. DOI: 10.1007/978-3-319-19069-3_4.
Autores: Cristina Cabanillas / David Knuplesch / Manuel Resinas / Manfred Reichert / Jan Mendling / Antonio Ruiz-Cortés /
Actual process executions may constitute a valuable input for improving process design. Process mining provides methods for automatic process analysis, among others for discovering processes by extracting knowledge from event logs in the form of a process model. Various algorithms are available to discover models capturing the control flow of a process, related to the behavioural perspective of the process.For perspectives like the organisational perspective, which manages the involvement of human resources in processes, only partial solutions for mining had been developed despite the importance of resource information not only for performance but also for compliance analysis.
Prior work on mining resource information focused on discovering specific aspects of the organisational perspective such as role models, separation of duty or social networks. However, comprehensive and integrated support for the wellestablished workflow resource patterns, and specifically in this context for the socalled creation patterns, was missing. Furthermore, the close interplay between the organisational and the behavioural perspectives (cross-perspective patterns) was disregarded.
The research reported in this paper presented an efficient and effective framework for mining the organisational perspective of business processes that is divided into an event log pre-processing phase, a phase for integrated resource mining including cross-perspective patterns, and a model post-processing phase. We evaluated our approach with an implementation of the three phases, with simulation experiments for measuring performance, and with the application of the approach on a real-life event log for checking its effectiveness.
Autores: Stefan Schönig / Cristina Cabanillas / Stefan Jablonski / Jan Mendling /
Process Performance indicators (PPIs) play an important role in monitoring the performance of operational procedures. Both defining and measuring suitable PPIs are key tasks for aligning strategic business objectives with the operational implementation of a process. A major challenge in this regard is that perspectives on the same real-world phenomenon differ among the stakeholders that are involved in these tasks. Since the formulation of PPIs is typically a managerial concern, there is a risk that these do not match with the exact operational and technical characteristics of business processes. To bridge this gap, the concepts described in PPIs must first be linked to their corresponding process elements. Establishing these links is paramount for the monitoring of process performance.
Without them, the values of PPIs cannot be computed automatically. However, the necessary links must currently be established manually. A task which is tedious and error-prone, due to the aforementioned incoherence between the different perspectives. The goal of our work is to overcome the efforts involved in the manual creation of links by automating this step. To achieve this, we developed an approach that automatically aligns textual PPI descriptions to the relevant parts of a process model. The approach takes a textual PPI description and a process model to which the PPI relates as input. Given this input, the approach generates an alignment in three steps. (1) Type classification: We make use of a decision tree classifier to identify the type of a given PPI, which is important because it affects the number and kinds of process model elements that should be aligned to a PPI. (2) PPI parsing: We parse the textual PPI description to extract those phrases that relate to specific parts of a process, making use of natural language processing techniques. (3) Alignment to process model: Finally, given the identified measure type and the extracted phrases, we compute an alignment between the phrases and the process model. A quantitative evaluation with a set of 173 PPIs obtained from industry and reference frameworks, demonstrates that our automated approach produces satisfying results.
Autores: Han van der Aa / Adela del-Río-Ortega / Manuel Resinas / Henrik Leopold / Antonio Ruiz-Cortés / Jan Mendling / Hajo Reijers /