The model judge : a tool for supporting novices in learning process modeling

Process models are a fundamental element in the BPM lifecycle. Hence, it is of paramount importance for organizations to rely on high-quality, accurate and up-to-date process models, to avoid taking decisions on the basis of a wrong picture of the reality. In this demo we present modeljudge.cs.upc.edu, a platform to boost the training of novice modelers when confronted with the task of translating a textual description into a process model in BPMN notation. The platform is integrated with Natural Language Processing (NLP) analysis and textual annotation, together with a novel model-to-text alignment technique. By using this platform, a novice modeler will receive diagnostics in real-time, which may contribute to a more satisfactory modeling experience.Peer ReviewedPostprint (published version

Challenges and opportunities of applying natural language processing in business process management

The Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. At the same time, NLP has reached a maturity level that enables its widespread application in many contexts, thanks to publicly available frameworks. In this position paper, we show how NLP has potential in raising the benefits of BPM practices at different levels. Instead of being exhaustive, we show selected key challenges were a successful application of NLP techniques would facilitate the automation of particular tasks that nowadays require a significant effort to accomplish. Finally, we report on applications that consider both the process perspective and its enhancement through NLP.Peer ReviewedPostprint (published version

Formal Reasoning on Natural Language Descriptions of Processes

The existence of unstructured information that describes processes represents a challenge in organizations, mainly because this data cannot be directly referred into process-aware ecosystems due to ambiguities. Still, this information is important, since it encompasses aspects of a process that are left out when formalizing it on a particular modelling notation. This paper picks up this challenge and faces the problem of ambiguities by acknowledging its existence and mitigating it. Specifically, we propose a framework to partially automate the elicitation of a formal representation of a textual process description, via text annotation techniques on top of natural language processing. The result is the ATDP language, whose syntax and semantics are described in this paper. ATDP allows to explicitly cope with several interpretations of the same textual description of a process model. Moreover, we link the ATDP language to a formal reasoning engine and show several use cases. A prototype tool enabling the complete methodology has been implemented, and several examples using the tool are provided.Peer ReviewedPostprint (author's final draft

Model-Agnostic process modelling

Modeling techniques in Business Process Management often suffer from low adoption due to the variety of profiles found in organizations. This project aims to provide a novel alternative to BPM documentation, ATD, based on annotated process descriptions in natural language

Aligning Textual and Graphical Descriptions of Processes Through ILP Techniques

With the aim of having individuals from different backgrounds and expertise levels examine the operations in an organization, different representations of business processes are maintained. To have these different representations aligned is not only a desired feature, but also a real challenge due to the contrasting nature of each process representation. In this paper we present an efficient technique for aligning a textual description and a graphical model of a process. The technique is grounded on using natural language processing techniques to extract linguistic features of each representation, and encode the search as a mathematical optimization encoded using Integer Linear Programming (ILP) whose resolution ensures an optimal alignment between both descriptions. The technique has been implemented and the experiments witness the significance of the approach with respect to the state-of-the-art technique for the same task.Peer Reviewe

Aligning textual and graphical descriptions of processes through ILP techniques

With the aim of having individuals from different backgrounds and expertise levels examine the operations in an organization, different representations of business processes are maintained. To have these different representations aligned is not only a desired feature, but also a real challenge due to the contrasting nature of each process representation. In this paper we present an efficient technique for aligning a textual description and a graphical model of a process. The technique is grounded on using natural language processing techniques to extract linguistic features of each representation, and encode the search as a mathematical optimization encoded using Integer Linear Programming (ILP) whose resolution ensures an optimal alignment between both descriptions. The technique has been implemented and the experiments witness the significance of the approach with respect to the state-of-the-art technique for the same task.Peer Reviewe