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

Bridging the gap between textual and formal business process representations

Tesi en modalitat de compendi de publicacionsIn the era of digital transformation, an increasing number of organizations are start ing to think in terms of business processes. Processes are at the very heart of each business, and must be understood and carried out by a wide range of actors, from both technical and non-technical backgrounds alike. When embracing digital transformation practices, there is a need for all involved parties to be aware of the underlying business processes in an organization. However, the representational complexity and biases of the state-of-the-art modeling notations pose a challenge in understandability. On the other hand, plain language representations, accessible by nature and easily understood by everyone, are often frowned upon by technical specialists due to their ambiguity. The aim of this thesis is precisely to bridge this gap: Between the world of the techni cal, formal languages and the world of simpler, accessible natural languages. Structured as an article compendium, in this thesis we present four main contributions to address specific problems in the intersection between the fields of natural language processing and business process management.A l’era de la transformació digital, cada vegada més organitzacions comencen a pensar en termes de processos de negoci. Els processos són el nucli principal de tota empresa i, com a tals, han de ser fàcilment comprensibles per un ampli ventall de rols, tant perfils tècnics com no-tècnics. Quan s’adopta la transformació digital, és necessari que totes les parts involucrades estiguin ben informades sobre els protocols implantats com a part del procés de digitalització. Tot i això, la complexitat i biaixos de representació dels llenguatges de modelització que actualment conformen l’estat de l’art sovint en dificulten la seva com prensió. D’altra banda, les representacions basades en documentació usant llenguatge natural, accessibles per naturalesa i fàcilment comprensibles per tothom, moltes vegades són vistes com un problema pels perfils més tècnics a causa de la presència d’ambigüitats en els textos. L’objectiu d’aquesta tesi és precisament el de superar aquesta distància: La distància entre el món dels llenguatges tècnics i formals amb el dels llenguatges naturals, més accessibles i senzills. Amb una estructura de compendi d’articles, en aquesta tesi presentem quatre grans línies de recerca per adreçar problemes específics en aquesta intersecció entre les tecnologies d’anàlisi de llenguatge natural i la gestió dels processos de negoci.Postprint (published version

Comparació de descripcions textuals i formals de models de processos

En el Business Process Management els processos de negoci sovint es documenten a la vegada com text i diagrama formal, afavorint les inconsistències. L'objectiu d'aquest projecte és desenvolupar un algorisme que permeti calcular la similitud entre un model BPMN formal i una descripció textual.In Business Process Management processes are usually documented both in formal and natural languages, incurring in inconsistencies. The goal of this project is to develop an algorithm capable of computing a similarity score between a formal BPMN model and a text document

NLP4BPM : Natural language processing tools for business process management

Business Process Management is facing a drift in the way process information is used within an organization. To reach a wide audience, organizations keep parallel representations of process information, thus making the processes to be understandable by everyone. However, this poses a challenge on the synchronization and transformation between different process representations. NLP4BPM is an environment to support such crucial tasks. It combines Natural Language Processing tools with process-oriented techniques, and through its web-based interface can be easily accessed from any device.Peer ReviewedPostprint (author's final draft

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

Unleashing textual descriptions of business processes

Textual descriptions of processes are ubiquitous in organizations, so that documentation of the important processes can be accessible to anyone involved. Unfortunately, the value of this rich data source is hampered by the challenge of analyzing unstructured information. In this paper we propose a framework to overcome the current limitations on dealing with textual descriptions of processes. This framework considers extraction and analysis and connects to process mining via simulation. The framework is grounded in the notion of annotated textual descriptions of processes, which represents a middle-ground between formalization and accessibility, and which accounts for different modeling styles, ranging from purely imperative to purely declarative. The contributions of this paper are implemented in several tools, and case studies are highlighted.This work has been supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.Peer ReviewedPostprint (author's final draft

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 model-based process descriptions

Process model descriptions are an ubiquitous source of information that exists in any organization. To reach different types of stakeholders, distinct descriptions are often kept, so that process understandability is boosted with respect to individual capabilities. While the use of distinct representations allows more stakeholders to interpret process information, it also poses a considerable challenge: to keep different process descriptions aligned. In this paper, a novel technique to align process models and textual descriptions is proposed. The technique is grounded on projecting knowledge extracted from these two representations into a uniform representation that is amenable for comparison. It applies a tailored linguistic analysis of each description, so that the important information is considered when aligning description’ elements. Compared to existing approaches that address this use case, our technique provides more comprehensive alignments, which encompass process model activities, events and gateways. Furthermore, the technique, which has been implemented into the platform nlp4bpm.cs.upc.edu, shows promising results based on experiments with real-world data

Supporting the process of learning and teaching process models

The creation of a process model faces the challenge of constructing a syntactically correct entity which accurately reflects the semantics of the reality, and is understandable. This paper proposes a framework called ModelJudge , focused towards the two main actors in the process of learning process model creation: novice modellers and instructors. For modellers, the platform enables the automatic validation of the process models created from the textual description, providing explanations about quality issues in the model. ModelJudge can provide diagnostics regarding model structure, writing style, and seman- tics by aligning annotated textual descriptions to models. For instructors, the platform facilitates the creation of modelling exercises by providing an editor to annotate the main parts of a textual description, that is empowered with Natural Language Processing (NLP) capabilities so that the annotation effort is minimized. So far around 300 students, in process modelling courses of five different universities around the world have used the platform. The feedback gathered from some of these courses shows good potential in helping students to improve their learning experience, which might, in turn, impact process model quality and understandability. Moreover, our results show that instructors can benefit from getting insights into the evolution of modeling processes including arising quality issues of single students, but also discover tendencies in groups of students. Although the framework has been applied to process model creation, it could be extrapolated to other contexts where the creation of models based on a textual description plays an important role.This work has been partially supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.Peer ReviewedPostprint (author's final draft